5G

5G White Paper：Key Technologies and Solutions for 5G HSR

1


Executive Summary After more than 30 years of explosive development, the mobile communication has become an information infrastructure connecting human society. The main challenge for 5G is to provide more than 100Mbps user experienced data rate with guaranteed service continuity anytime and anywhere, regardless of static state or high-speed moving, and coverage center or coverage edge. The High Speed Railway (HSR) is an important yet extremely challenging scenario for future 5G wireless communication systems. By the operator’s current network testing, we find that the challenges mainly come from five aspects. (1) Mobility and handover aspects: these two issues both come from the fast time-varying of the HSR wireless channel because of the Doppler frequency offset. Considering different solution types, mobility aspects only refer to the L1 related key technologies and solutions, while handover aspects mean the L2 related solutions in this white paper. (2) Coverage aspects: this issue comes from the large penetration loss of the train, inter site distance of the deployment, low transmit power of the user equipment and etc. (3) Capacity aspects: higher active user rate than the public network and dense distribution of the passengers require larger capacity in the HSR scenario. (4) Energy efficient: considering the CAPEX, green communication is more preferable from the operator’s perspective. Motivated by the above issues, this white paper aims to identify some potential key technologies and solutions. Compared with the 1st edition of the white paper, not only some new technologies and solutions from the academic perspective are included, but also the latest agreements and trend in 3GPP 5G NR discussion are also captured in this white paper. A brief summary of the main solutions are given below: 

Mobility aspects: –

Unlike 4G LTE single numerology, an optimized numerology including the sub-carrier spacing and cyclic shift are designed. At the same time, different sub-carrier spacing and CP combination under different frequency range are also proposed.

–

To effectively estimate the fast time-varying channel parameters on HSR, a historical information based basis expansion model (HiBEM) is proposed. Moreover, the candidate DM-RS patterns in 3GPP 5G NR are also summarized.

–

Based on the current discussion in 3GPP, it is better to adopt CP-OFDM in DL and DFT-S-OFDM in UL for high mobility scenario.

–

Because of the fast time variation of the fading channel in HSR scenario, signals are dispersed in the frequency domain due to the Doppler effects. The Doppler diversity transmission under imperfect CSIT is proposed in order to explore the additional degree-of-freedom.

–

By the design of the new cyclic shift restriction set in the PRACH procedure, the performance of random access can be improved.



Handover aspects –

In order to reduce the handover failure rate and improve the user experience of high speed UEs, a UE mobility identity-based handover procedure are proposed.

–

To improve the handover performance without increasing total energy consumption, a novel handover with smart power adjustment is presented, which is proposed from the perspective of reducing the “uncertainty” in handovers.



Coverage aspects 1

5G White Paper：Key Technologies and Solutions for 5G HSR –

A time-domain power allocation scheme based on  fairness is presented to mitigate the near far effect in HSR scenario and the effect is validated by the numerical simulation.



Capacity aspects –

To improve the capacity, each RRH along the railway can create a directional narrow analog beam along the railway track, and another antenna panel is equipped on top of a train, generating a directional beam towards the RRH. The short distance between the RRH and the train would generate strong beamforming gain.

–

The instantaneous channel state information is essential for improving the capacity. The accuracy of channel estimation can be improved via modifying the outdated channel based on the position information of the train. By exploiting the partial channel and the position of the antenna array, an accurate channel matrix could be constructed based on the spatial-temporal correlation of the channel.

–

When some channel conditions are particularly poor, it is not necessary to select antenna to transmit the signal. An adaptive spatial modulation scheme embedded Alamouti space-time block coding is proposed for the HSR scenario to provide larger diversity gain and better error performance.



Energy efficient aspects –

We give a train arrival information based energy utilization control mechanism. The main idea is to determine whether close the dedicated cell and extend the coverage of the nearby public cell based on the arrival state of the train for the dedicated cell.

Finally, the HSR channel model including the low-frequency and high-frequency band are also provided in the appendix. Although many schemes are proposed to enhance the performance, many challenges still remain and new solutions are encouraged to be proposed by joint effort of academia and industry to have a better performance for 5G HSR scenario.

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Table of Contents EXECUTIVE SUMMARY ................................................................................................................................................................... 1 1.

INTRODUCTION ....................................................................................................................................................................... 5

2.

HSR SCENARIO AND TECHNICAL REQUIREMENTS ..................................................................................................... 5 2.1 HSR SCENARIO MODEL AND PARAMETERS IN 3GPP NR ................................................................................... 5 2.2 MAIN ISSUES FOUND BY THE TEST OF PRESENT NETWORK ................................................................................ 7

3.

MOVING NETWORK FOR HSR SCENARIO .................................................................................................................... 10

4.

KEY TECHNOLOGIES AND SOLUTIONS FOR 5G HSR ................................................................................................ 11 4.1 HIGH MOBILITY ORIENTED KEY TECHNOLOGIES AND SOLUTIONS................................................................... 11 4.1.1 Numerology Design ................................................................................................................................ 11 4.1.2 Channel Estimation and DM-RS Design ................................................................................................ 14 4.1.3 Waveform ............................................................................................................................................... 22 4.1.4 Doppler Diversity Transmission ............................................................................................................. 22 4.1.5 PRACH Enhancement ............................................................................................................................ 23 4.2 HANDOVER ORIENTED KEY TECHNOLOGIES AND SOLUTIONS FOR 5G HSR..................................................... 28 4.2.1 UE Mobility Identity-based Handover Solution ..................................................................................... 28 4.2.2 Power Adjustment Assisted Handover ................................................................................................... 29 4.3 COVERAGE ORIENTED KEY TECHNOLOGIES AND SOLUTIONS FOR 5G HSR ..................................................... 32 4.3.1 Time-domain Power Allocation for Enhancing Near-far Coverage........................................................ 32 4.4 CAPACITY ORIENTED KEY TECHNOLOGIES AND SOLUTIONS FOR 5G HSR ...................................................... 34 4.4.1 Fixed Beam Direction based Transmission............................................................................................. 34 4.4.2 Location-aided MIMO Transmission ...................................................................................................... 34 4.4.3 Space-Time-Coded Adapticve Spatial Modulation for HSR .................................................................. 35 4.5 ENERGY EFFICIENT ORIENTED KEY TECHNOLOGIES AND SOLUTIONS FOR 5G HSR ........................................ 37 4.5.1 Train Arrival Information based Energy Utilization Control .................................................................. 37

5.

SUMMARY ............................................................................................................................................................................... 39

6.

APPENDIX I: HIGH SPEED CHANNEL MODEL ............................................................................................................. 40 A1 HIGH SPEED RAILWAY CHANNEL MODEL AT LOW FREQUENCY BANDS ............................................................... 40 A1.1 Path Loss ................................................................................................................................................. 40 A1.2 Amplitude Distribution of Small-Scale Fading ....................................................................................... 41 A2 5G MMWAVE HIGH-SPEED CHANNEL CHARACTERIZATION AND RAY-TRACING ................................................ 42 A2.1 Scenario Modules of 5G mmWave High-Speed-Railway Channels ....................................................... 42 A2.2 5G mmWave HSR Channel Characterization ......................................................................................... 45

REFERENCE ..................................................................................................................................................................................... 47 ABBREVIATION ............................................................................................................................................................................. 49 ACKNOWLEDGEMENT ................................................................................................................................................................. 51

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1. Introduction The mobile communication has become an information infrastructure connecting human society, after more than 30 years of explosive development. Motivated by mobile internet and internet of things, the next generation mobile network needs to provide diversified services and satisfy extremely user experience. Seamless wide-area coverage, high- capacity hot-spot, low-latency high-reliability and low-power massive-connection are the four typical technical scenarios that derived from the main application scenarios and service requirements. The seamless wide-area coverage is the basic scenario. The main challenge for 5G is to provide more than 100Mbps user experienced data rate with guaranteed service continuity anytime and anywhere, regardless of static state or high-speed moving, and coverage center or coverage edge. This white paper aims at identifying the main issues found by the present network testing and requirements of 5G HSR scenario, and providing potential solutions to fulfill such requirements.

2. HSR Scenario and Technical Requirements With the development of global high speed railway, China has achieved spectacular growth in high speed technology. By the end of 2016, the total running mileage of high speed railway in China has reached to 22 thousand kilometers, and more than 5 billion passengers have taken the railway. The mobile data traffic of travelers is usually related to their travelling time. It has been estimated that the annual data traffic of high speed railway travelers will be more than 2 trillion MB. Therefore, high speed railway is an important scenario of wireless communication in China and even the whole world. Currently the maximum mobility speed supported by 3rd Generation Partnership Project (3GPP) is 350km/h. However, the requirement for 5G-NR is to support for up to 500 km/h speed. In high speed railway scenario, in order to satisfy the diversified Key Performance Indicators (KPIs), e.g., the user experience rate, system spectrum efficiency, and handover success rate, it is necessary to enhance the whole performance of high speed railway wireless communication. Moreover, some problems in the present network should also be addressed.

2.1

HSR Scenario Model and Parameters in 3GPP NR

In high speed railway scenario, the deployment issue focuses on continuous coverage along track in high speed railway. The key characteristics of this scenario are consistent passenger user experience and critical train communication reliability with very high mobility. 3GPP has identified the deployment in this scenario, as illustrated in the following fig. 2-1[1].

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（a）4GHz SFN deployment

（b）4GHz non-SFN deployment

（c）30GHz

deployment

Fig. 2-1 HSR network topology in 3GPP NR. Some of its parameters are listed in Table 2-1 [2].

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5G White Paper：Key Technologies and Solutions for 5G HSR Table 2-1 Baseline Parameters of High Speed Scenario Attributes

Values or assumptions

Carrier Frequency

Macro only: Around 4GHz

NOTE1

Macro + relay nodes: 1) For BS to relay: Around 4 GHz For relay to UE: Around 30 GHz or Around 70 GH or Around 4 GHz 2) For BS to relay: Around 30 GHz For relay to UE: Around 30 GHz or Around 70 GHz or Around 4 GHz

Aggregated system

Around 4GHz: Up to 200 MHz (DL+UL)

bandwidth NOTE3

Around 30GHz or Around 70GHz: Up to 1GHz (DL+UL)

Layout

Macro only: -

Around 4GHz: Dedicated linear deployment along the railway line RRH site to railway track distance: 100m

Macro + relay nodes: -

Around 4GHz: Dedicated linear deployment along the railway line. RRH site to railway track distance: 100m

-

Around 30GHz: Dedicated linear deployment along the railway line RRH site to railway track distance: 5m.

ISD

-

Around 4GHz: ISD 1732m between RRH sites, two TRPxs per RRH site.

-

Around 30GHz: 1732m between BBU sites, 3 RRH sites connected to 1 BBU, one TRP per

RRH site, inter RRH site distance (580m, 580m, 572m). -

Small cell within carriages: ISD = 25m.

BS antenna elements

Around 30GHz: Up to 256 Tx and Rx antenna elements

NOTE4

Around 4GHz: Up to 256 Tx and Rx antenna elements

UE antenna elements

Relay Tx: Up to 256 antenna elements

NOTE4

Relay Rx: Up to 256 antenna elements Around 30GHz: Up to 32 Tx and Rx antenna elements Around 4GHz: Up to 8 Tx and Rx antenna elements

User distribution and

100% of users in train

UE speed

For non-full buffer, 300 UEs per macro cell (assuming 1000 passengers per high-speed train and at least 10% activity ratio) Maximum mobility speed: 500km/h

Service profile

Alt 1: Full buffer Alt 2: FTP model 1/2/3 with packet size 0.5 Mbytes, 0.1 Mbytes (other value is not precluded) Other traffic models are not precluded, e.g., for critical train communications.

2.2

Main Issues found by the Test of Present Network

From the operator’s current network testing, it is found that the main problems of the communication system design of the high speed scenarios are shown as follows:

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

Handover aspects Table 2-2: The problems of handover and the potential enhancement scheme of the present network The problems and reason

–

Potential network enhancement scheme

The connection failure rate of the connected

–

RRU cascading

user is high

–

Relays deployed at the top of the train

–

The IDLE/Inactive UEs’ paging loss rate is high

–

Enhancement of RRM indicators ( to reduce

–

The longer setup time of calling due to the

the time required for cell reselection and

HOF/paging loss

switching)

–

The sudden signal flushing due to mobility



Mobility aspects Table 2-3: Mobility problems and potential solutions to enhance the present network The problems and reason

–

–

Potential network enhancement scheme –

Doppler frequency offset effect (affecting downlink data rate, resulting in PRACH

enhancement (higher frequency offset

Message2 not received)

estimation range, based on CP, cell-level

The signal quality (RSRP and SINR) changes

predistortion frequency estimation) –

dramatically (normal macro network fluctuation range +/- 3dB, HSR scene +/- 9dB), the present

PRACH enhancements (R14 introduces new Cyclic shift compatible sets)

–

BS scheduling program is unable to track, MCS selection is not accurate, resulting in further

More reliable MCS and RANK selection methods

–

decline in user experience rate –

Frequency offset compensation algorithm

The AMR of eVoLTE can not be applied (IMS does not know the status of the CSI, the BS can not decode the IMS)

8

Uplink scheduling via SRS




Coverage aspects Table 2-4：Coverage issues and potential present network enhancement programs

The problems and reason –


High penetration of the special body structure of the high-speed rails and the coverage of the UEs

BS-railway distance and other indicators –

in the train caused by the blockage of the front

–

A new "HSF beamforming" scheme for

and rear seats (the average signal strength of the

suppressing "horizontal zero depression"

UEs close to the windows is 10dB higher than

problem

that of the UEs close to the windows aisle)

–

On-top Relay

When some trains come close to the BS-pole in

–

BS 2T4R / 4T4R

high speed, the antenna "horizontal zero"

–

High power terminal

problem led to an average signal strength and

–

Improvement of uplink power control

quality decreased by 5 ~ 8dB –

Optimization of BS height, BS spacing and

algorithm –

VoLTE packet loss problem caused by defects in upward operation

The 3dB point of antenna horizontal beam focus on the center point of two BSs and the beam pointing to the outside



Capacity aspects Table 2-5：Capacity issues and potential network enhancement programs

The problems and reason –

–


Part of the local public network users access, resulting in some private network users

private network UEs priority scheduling and

experience significantly decreased

other technical schemes –

UE activation rate of high-speed rail is much larger than other scenes

–

The public network UEs to move out and

System spectrum efficiency is low

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The optimization of sub-frame ratio of HSR


3. Moving Network for HSR Scenario With the growing of various data traffic requirements, the heterogeneous wireless communication network is widely recognized as one of the core components of the next generation rail traffic communication system. However, due to the system’s complexity and the radio resource’s scarcity, the current network might not able to effectively and reliably transmit the control and traffic data. To solve the problem, this document introduces the Moving Network architecture into the rail traffic communication system, and establish a “moving base station + fixed base station” based novel network structure. Based on our theoretical and experimental study, the newly proposed model can handle the issues coming from random interference, mobility management, and resource allocation. Since the first Japanese Shinkanen Line went to operation in 1954, the international high speed railway (HSR) had evolved into a technology system consists of Japanese Shinkansen, France TGV, and Germany ICE, after 50 years modification and improvement. The design scheme of Chinese HSR system was firstly proposed to the government in 1990, and being established since the end of the 90s. After entering the 21st century, the development speed of Chinese HSR system is significant. The initialize of Beijing-Tianjing HSR line’s operation officially marked that China had enter the time of HSR. According to the statistic results, the total length of Chinese HSR line for passenger traffic had reached 9356 km at the end of 2012, which is longer than the sum of all the other international countries. The Chinese HSR had become a new component in the international HSR technology system. Among all the technical challenges of HSR, the controlling signal’s efficient and reliable transmission had been studied for quite a long time. Generally, the HSR system is radio spectrum limited, but presents unique high mobility, requires high Quality of Service (QoS), and high Reliability, Availability, Maintainability and Safety (RAMS), which actually matches the classic features of the future wireless communication system proposed in METIS projects. However, considering the scarcity of wireless resource, the special radio propagation characteristics, and the heterogeneity of wireless communication terminals in future HSR wireless communication network, at least there are three aspects of core challenges need be addressed. First, the great randomness (channel, traffic, location, QoS, etc.,) makes the interference between communication links more difficult to be predicted and managed with traditional network architecture or existing schemes. Second, although the train itself is moving along the fixed railway, the wireless terminal’s mobility management still need considerable improvement, which is normally represented as a good signal strength but low data rate when taking HSR in a journey. Last but not least, the unique node distribution feature and the special requirements of traffic and service requires completely new design of the radio resource management for HSR communication systems. Inspired by the well-known method to deploy train-based antenna systems for avoiding the penetration lost caused by metal train shell, we proposed to treat the mobile terminals distributed within a train cartage or even the whole train as a whole, which makes it a Moving Network. This simple idea actually creates a series of advantages for improving and implementing high efficient PHY layer above protocols/schemes. First, by treating all the terminals as a whole, the burden for the rail-side infrastructures, e.g., the base station (BS), to handle the high mobility generated signaling is significantly reduced. In this way, the BS station can allocate more resources to conduct more complicated processing algorithms for maintain much better performance between itself and the moving network entity. Second, the moving network architecture can be used as a nature structure to apply the latest cloud or edge-cloud services for full filling the huge amount of data-centric applications from either passenger or the monitor systems for the train. Last but not least,

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5G White Paper：Key Technologies and Solutions for 5G HSR the moving network architecture also created new angle for communication service providers to create new services or possibilities to increase their revenue. Generally, the existing study related to moving network architecture had been conducted in three different aspects. First, a completely new theoretical interference modeling method had been developed specifically for the moving scenario, which provided an easy way to obtain the performance bound for one specific network settings or potential services[3-4]. Second, a mobility management framework had been designed for the “moving network + fixed rail side infrastructure” system, which significantly reduced the signaling complexity, and also created new possibility to support new services. Finally, new radio resource management schemes had been designed for the HSR system based on the moving network structure, which support the mixed traffic generated from passengers, monitoring systems, and the controlling systems.

4. Key Technologies and Solutions for 5G HSR 4.1 High Mobility Oriented Key Technologies and Solutions 4.1.1 Numerology Design 4.1.1.1 Subcarrier Spacing Design As 5G is expected to be deployed in higher carrier frequency than LTE, the mobility of a terminal would bring a larger Doppler shift (fd = v ∗ f/c), especially in high speed scenario. To mitigate the Inter-Carrier Interference (ICI) caused by Doppler shift, larger subcarrier spacing is required for high speed railway scenario. As shown in Table 4-1, for example, a subcarrier spacing is chosen to be 20 times of the Doppler shift[5-6], we can see that the 15kHz subcarrier spacing applied in LTE cannot meet the target mobility of 500km/h even under 2.6GHz carrier frequency. Table 4-1 Maximum Doppler shift and required subcarrier spacing

Velocity=500km/h Carrier Frequency (GHz)

Maximum shift (kHz)

Doppler

20 ∗ fd

Subcarrier spacing (kHz)

2

0.926

18.52

30

2.6

1.2

24

30

3.5

1.62

32.4

60

4

1.85

37

60

In following Figure 4-1, we show the impact of Doppler shift on SIR under different subcarrier spacing. The SIR is calculated as [7]:

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1 1  2 2 I0     f   m  k    m k 

SIR 

where, f ( ) 

1 N



1 e 1 e

j 2 

j ( 2 / N ) 

(4-1)

denotes the ICI.   f d / f sc , f sc denotes the subcarrier spacing, N denotes the FFT

size in transmitter, m and k denote the subcarrier index. From Fig.4-1, it is observable that a larger subcarrier could greatly mitigate the ICI.

55 fsc=15kHz fsc=30kHz fsc=45kHz fsc=60kHz

50 45 40

SIR [dB]

35 30 25 20 15 10 5

0

500

1000 1500 Doppler[Hz]

2000

2500

Fig. 4-1 The impact of Doppler shift on SIR under different subcarrier spacing.

4.1.1.2 Cyclic Prefix Design To mitigate the Inter-Symbol Interference (ISI) caused by multi-path delay spread, Cyclic Prefix (CP) is inserted in front of each OFDM symbol. In high speed railway scenario of 5G, a larger subcarrier spacing is possibly applied to mitigate the ICI, but this leads to a shorter CP length and would be insufficient to cover the delay spread in certain periods. Therefore, we need to define two different types of CP length in 5G, a type with longer CP length (aka, extended CP) to mitigate the ISI under large/medium delay spread and the other type with shorter CP length (aka, normal CP) to increase the spectrum efficiency under small delay spread. Figure 4-2 shows the impact of different CP lengths on BLER performance under the assumption of normalized spectrum efficiency within a TTI (0.5ms). Ideal Channel Estimation (TDL-C [100ns])

0

10

-1

-1

10

BLER

BLER

10

-2

10

30kHz NCP 60kHz NCP 60kHz ECP

-3

10

12

-2

10


-3

10

-4

-4

10

Ideal Channel Estimation (TDL-C [300ns])

0

10

10 14

16

18 20 SNR (dB)

22

24

26

12

12

14

16

18 20 SNR (dB)

22

24

26

5G White Paper：Key Technologies and Solutions for 5G HSR Ideal Channel Estimation (TDL-C [1000ns])

0

BLER

10

-1

10


-2

10

12

14

16

18 20 SNR (dB)

22

24

26

Fig. 4-2 The impact of different CP lengths on BLER performance under normalized spectrum efficiency.

4.1.1.3 Summary Up to now, the standardization of 3GPP NR has agreed that multiple numerologies would be supported [8]. A given numerology consists of subcarrier spacing and corresponding cyclic prefix. The detailed numerology set is listed in Table 4-2. Note that, only 60kHz subcarrier spacing supports both normal CP and extended CP currently. Table 4-2 Supported numerology set in 3GPP NR

f  2  15 [kHz]



Cyclic prefix

0

15

Normal

1

30

Normal

2

60

Normal, Extended

3

120

Normal

4

240

Normal

5

480

Normal

Besides the progress in RAN1 aspect, 3GPP RAN4 has specified that for sub-6GHz, the three subcarrier spacing, 15kHz, 30kHz and 60kHz are supported, as shown in Table 4-3. Table 4-3 Supported numerology set under different frequency range

Frequency range

SCS (kHz)

Min (MHz)

Range 1

15

5

Range 1

30

Option 1: 5MHz Option 2: 10MHz

100

Range 1

60

Option 10MHz Option

100MHz

13

CHBW

Max (MHz) 50

1: 2:

CHBW


20MHz Range 2

60

50

200

Range 2

120

50

400MHz

Based on the above analysis and evaluation, the target mobility of 500km/h in high speed railway scenario is supportive via deploying larger subcarrier spacing, e.g., 30kHz or 60kHz. If 60kHz subcarrier spacing is adopted, it is suggestive to apply extend CP to cover the delay spread and thus guarantee the cell coverage and system performance. Moreover, considering the limited coverage of higher carrier frequency, a lower carrier frequency is recommended to be adopted for high speed railway scenario.

4.1.2 Channel Estimation and DM-RS Design 4.1.2.1 A Channel Estimation Method - HiBEM Channel estimation is of vital importance for wireless communication systems on HSR. However, the traditional channel estimators such as linear minimum mean square error (LMMSE) and least square (LS) fails to provide satisfactory estimation performance for wireless systems on HSR due to the complicated HSR scenarios and the fast variation nature of wireless channels on HSR [9]. To effectively estimate the fast time-varying channel parameters on HSR, a historical information based basis expansion model (HiBEM) is proposed. The performances of HiBEM as well as traditional channel estimators are investigated at three typical HSR scenarios. Basis expansion model (BEM), a popular model for time-varying channel approximation, decomposes the channel into the superposition of the time-varying basis functions weighted by time-invariant coefficients. BEM expresses the time-varying channel h=[h1,h2,…,hN]T as

h  Bg where B  b1 , b 2 ,

g   g1 , g 2 ,

, gQ 

(4-2)

, bQ  represents the basis matrix, b q 1  q  Q  denotes the qth basis vector, and T

is the BEM coefficients to be estimated. Clearly, the dimension of the estimated parameters

can be decreased from N to Q. Existing BEMs include complex-exponential BEM (CE-BEM), polynomial BEM (P-BEM). All of the existing BEMs have unvaried basis matrices, which are not suitable for various HSR scenarios. Noting that the trains follow the fixed tracks on HSR and the railway surroundings remain the same at the same location. Therefore, wireless channels in a fixed position of different trains are strongly related and the correlation would remain unchanged over time only if the environment and transceiver parameters are unvaried. Based on these observations, we propose HiBEM [10]. The HiBEM adopts the first Q eigenvectors of channels’ autocorrelation matrix as its basis matrix. The autocorrelation matrix is calculated from

R h   hh H 

14

(4-3)


Take the singular value decomposition of R h will yield

R h  UVD

(4-4)

Hence, we can construct the basis matrix out from U , whose columns are the eigenvectors of R h . The basis matrix of HiBEM can be written as

B  U :,1: Q 

(4-5)

R h1

1st train

...

...

Rh 

(k-1)th train

R hi 

R h ( k 1)

v kth train

... position Fig. 4-3 The scheme of estimation with historical information. One key problem is the computation of R h . Since the wireless channels on HSR has strong relativity in a fixed position, we utilize this characteristic to calculate the correlation matrix R h . Fig.4-3 depicts the scheme of calculating

R h . Supposing the current train is the kth train, we utilize the estimated channels of previous (k-1) trains at the same position to compute the corresponding correlation matrices R hi 1  i  k  1 and average them as

R   R hi  

1 k 1  R hi k  1 i 1

(4-6)

With HiBEM, the received signals can be expressed as

y  Xh  w  XBg  w

(4-7)

where X is a diagonal matrix of which diagonal elements are transmitted signals, and w represents the zero mean additive white Gaussian noise (AWGN). Denote the equivalent equation in pilot position as

y p  X pB pg  w p  A pg  w p

(4-8)

where A p  X p B p is a P  Q matrix and P is the length of pilots. With the basis matrix B calculated form the historical information, the basis coefficients can be obtained by

gˆ  A†p y p

(4-9) 15

5G White Paper：Key Technologies and Solutions for 5G HSR Thus the estimated channel can be expressed as

hˆ  Bgˆ

(4-10)

The evaluation model shown in Fig. 4-4 is a typical HSR scenario with three cells deployed successively and the coverage areas are urban, cutting and viaduct environment, respectively. According to the network topology in 3GPP 5G-NR RAN-1, cells are classified into 3 categories: Micro with carrier frequency at 3.5 GHz, Relay with carrier frequency at 30 GHz and Macro with carrier frequency at 3.5 GHz. The detailed parameters are illustrated in Table 4-4. BS1

BS2

BS3

Urban

Cutting

Viaduct

Fig. 4-4 System model Table 4-4 Comparison of cell’s network topologies in the evaluation model

Architecture Parameters

Micro

Relay

Macro

Carrier frequency

3.5 GHz

30 GHz

3.5 GHz

System bandwidth

100 MHz

500 MHz

100 MHz

Distance between RRHs

572 m

572 m

1732 m

5m

5m

100 m

Height of RRH

2.5 m

2.5 m

30 m

Height of Relay

2.5 m

2.5 m

2m

Tx power 30 dBm

Tx power 43 dBm/10MHz

Tx power 27 dBm

Tx power 23 dBm

Turn

2 sectors with main lobe

Distance between RRH and Railway track

RRH Antenna UE Antenna Orientation of RRH

Tx

power

30

power

27

dBm Tx dBm Turn main lobe along the track

main

lobe

along the track

rotated

The real-time signal-to-noise ratios (SNRs) between the train and base stations of each categories are calculated by

SNR  dB   P   NT  N F 

(4-11)

where P is received power measured via deterministic modeling approach on the evaluation model. NT is the thermal noise power under normal temperature which can be represented as 16

NT  174  10  log10 W  .

It is

5G White Paper：Key Technologies and Solutions for 5G HSR associated with the system bandwidth W and -174 is the spectral noise power density for 1 Hz bandwidth (in dBm/Hz) at room temperature. The noise figure N F is set as 7 dB. Fig 4-5 shows real-time SNR values in evaluation models of three topologies, respectively.

Micro topology

Relay topology

Macro topology Fig. 4-5 Real-time SNR. Rician channels are constructed with the measured TDL parameters in each area in the link level estimation. We reuse the parameter configuration of long term evolution (LTE) and estimate channel using both exist and HiBEM estimator. The transmission frame structure of a transmission time interval (TTI) is shown in Fig. 4-6. Cell reference signal marked by yellow part is utilized for channel estimation.

17

5G White Paper：Key Technologies and Solutions for 5G HSR Symbol 1 2 3 4

Subcarrier

5 6 7 8 9 10 11 12 13 14

Data

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

CRS

Fig. 4-6 Frame structure in one slot. Fig. 4-7 illustrates the MSEs results of channel estimators versus SNR. As can be seen, HiBEM yields the best performance and LS estimator has the worst performance when SNR is less than 0 dB. HiBEM has substantially lower MSEs than LMMSE at low SNR region and has similar performance with LMMSE when the SNR is larger than 20 dB.

Fig. 4-7 MSE comparison between estimators. In order to evaluate the real-time throughput, each real-time SNR are mapped into the throughput-vs-SNR of particular area and calculate the throughput by linear interpolation to obtain real-time throughput. Fig. 4-8 presents the real-time throughputs of various channel estimators with three kinds of network topologies. It can be seen from Fig.4-8 that more than 90% snapshots of throughput exceed 100Mbps utilizing HiBEM estimator.

18


Micro topology

Relay topology

Macro topology Fig. 4-8 Real-time Throughputs.

4.1.2.2 DM-RS Design A larger Doppler shift in high speed railway scenario would cause fast channel variation in time domain, which means the channel coherent time is much reduced (the channel coherent time can be approximated as τ ≈

0.423 fd

). Based

on the coherent time formula, we can obtain the coherent time is about 230us with the mobility of 500km/h under 4GHz carrier frequency. In LTE, the time domain density of CRS is 4 symbols per TTI (1ms). The time interval between two CRS symbols is 250us, which exceeds the coherent time and is unable to provide reliable channel estimation in high speed railway scenario of 5G. Thus a higher DMRS density in time domain is required to ensure sufficient accuracy of channel estimation. In following, we evaluate the required time domain DMRS density within a TTI (0.5ms) for 30kHz and 60kHz subcarrier spacing under the assumption of normalized spectrum efficiency.

19


0

0

10

Realistic Channel Estimation (TDL-C [100ns/1000ns])

10

-1

10 -1

BLER

BLER

10

Ideal Est. Real Est./ Pattern 1a Real Est./ Pattern 1b Real Est./ Pattern 1c

-2

10

12

30kHz NCP

5 RS 7 RS 5 RS 7 RS

-3

10

symbols (100ns) symbos (100ns) symbols (1000ns) symbols (1000ns)

-4

-3

10

-2

10

10

14

16

18 20 SNR (dB)

22

24

26

60kHz+NCPc

12

14

16

18 20 SNR (dB)

22

24

26

30kHz+NCP

Fig. 4-9 Evaluation of different time domain DMRS density in high speed railway scenario. From the evaluation results in Figure 4-9, with normalized spectrum efficiency, the time domain DMRS density of 5 symbols per TTI (0.5ms) achieves the best BLER performance. Considering that the first 1~3 symbols within a TTI would be used for downlink control channel, at least 4 DMRS symbols is required for downlink data channel to guarantee channel estimation accuracy. Additionally, for a high speed train (HST) running in open space scenario, e.g., in rural place or on a viaduct, there usually exists a strong LOS path and less surrounding scatters between the BS and moving terminal [11]. In such scenario, it is considerable to reduce the time domain DMRS density. 3GPP RAN1 has agreed to deploy front-loaded DMRS design for DL data channel, in which the DMRS is mapped to first 1 or 2 symbols of data region, while additional DMRS can be configured on later part of the data region for higher Doppler scenario. The number of additional DMRS symbols and corresponding locations are also configurable via higher layer signaling. Several agreed DMRS patterns with additional DM-RS are shown in Figure 4-10, 4-11 and 4-12 [12]. The location of additional DMRS is depending on the slot format or the last symbol of PDSCH. Note that, the flexible part in the slot can be configured with either PDSCH transmission or non-PDSCH transmission. Up to now, additional DMRS symbols with same density in frequency domain compared to front loaded DMRS is supported in 3GPP NR. The necessity of reduced DMRS density in additional DMRS symbols still needs further discussion.

20


Symbol #7

Symbol #11

Symbol #(X+1) Symbol #(X+1)

Flexible

Symbol #9

Symbol #0 - #X (X=1/2)

NonPDSCH

DMRS

Symbol #(X+1)

PDSCH

Symbol #0 - #X (X=1/2)

Symbol #0 - #X (X=1/2)

Control

Fig. 4-10 DMRS pattern with 1 front-loaded DMRS symbol and 1 additional DMRS symbol. Flexible

Symbol #11

Symbol #9 Symbol #7


NonPDSCH

DMRS

Symbol #6

Symbol #0 - #X (X=1/2)

PDSCH

Symbol #0 - #X (X=1/2)

Control

Fig. 4-11 DMRS pattern with 1 front-loaded DMRS symbol and 2 additional DMRS symbols.

Symbol #11

Symbol #10


Flexible

Symbol #9


NonPDSCH

DMRS

Symbol #8

Symbol #0 - #X (X=1/2)

PDSCH

Symbol #0 - #X (X=1/2)

Control

Fig. 4-12 DMRS pattern with 2 front-loaded DMRS symbols and 2 additional DMRS symbols. 21


4.1.3 Waveform For sub-6GHz, at present 3GPP support CP-OFDM for Downlink and uplink, and the spectrum utilization should be larger than 90% in LTE. However, there is no specific spectrum constraint technology which will depend on the implementation. In addition, for single stream transmission, to improve the performance of edge UEs 3GPP also support DFT-S-OFDM waveform. Based on the above analysis, it is better to adopt CP-OFDM in DL and DFT-S-OFDM in UL for high mobility scenario.

4.1.4 Doppler Diversity Transmission In high mobility systems, the fast time variation of the fading channel can be utilized to provide time diversity or Doppler diversity, given that signals are dispersed in the frequency domain due to the Doppler effects. The Doppler effects provide an additional degree-of-freedom that can provide significant performance gains to high mobility communications. The maximum Doppler diversity order can be achieved by performing linear block precoding. The design criteria for linear precoders that can achieve the maximum Doppler diversity order is presented in [13]. It is shown that under a complex exponential BEM model, a linear precoder Θ∈C^(N×K) can achieve the maximum Doppler diversity if and only if there exist at least Q+1 non-zero entries in Θe, ∀e≠0, where Q=2[f_D NT_s] is the order of the BEM model, and K and N are the block length before and after precoding, respectively. Based on the above design criteria, several precoders are proposed in [13]. The tall Vandermonde precoder can achieve the maximum Doppler diversity for any modulation schemes, at the cost of some spectral efficiency loss because the coding rate is (K )/N < 1. A class of rate-1 square Vandermonde precoders with K=N can achieve the maximum Doppler diversity for QAM constellations. A rate-1 Doppler domain multiplexing (DDM) scheme proposed in [14] can approach the performance of a maximum Diversity system, and it is constellation independent. The above precoders are developed for SISO time-varying at fading channel. The precoder designs for system with doubly selective fading are discussed in [15], where the Doppler and frequency diversity can be obtained simultaneously. Space-time-Doppler block coding are proposed for MIMO at fading channel to achieve both space diversity and time diversity [16]. There are limited works devoted to the design of Doppler diversity transmissions with imperfect CSI. The performance of Doppler diversity transmission with imperfect CSI is studied in [17], where the Doppler diversity is achieved by means of spreading spectrum operations. The presence of channel estimation errors might destroy the optimality of transceivers designed for systems with perfect CSI. Therefore, new transceivers need to be developed by considering channel estimation errors. The statistical properties of channel estimation errors are explicitly considered in the optimum designs of several Doppler diversity systems [18]. In [18], the maximum Doppler diversity is achieved with a simple repetition code at the transmitter and an optimum combining receiver that considers the effects of channel estimation errors. The spectral efficiency of the repetition code is low. A spectral efficient Doppler diversity system with imperfect CSI is developed in [18]. Optimum and low complexity sub-optimum receivers are developed in [18] by 22

5G White Paper：Key Technologies and Solutions for 5G HSR considering channel estimation errors. The error probability of the system is expressed as explicit functions of channel estimation errors and Doppler shift, and they reveal the fundamental tradeoffs between Doppler diversity and channel estimation errors. Doppler diversity transmissions in high mobility systems are reviewed in this section from two aspects: fundamental performance limits and practical designs of Doppler diversity systems. It has been shown that there is a fundamental tradeoff between channel estimation errors and Doppler diversity order, given that fast time-varying fading means a higher Doppler diversity order, but it is more difficult to estimate and track the channel in high mobility scenarios. With properly designed pilot symbols, systems with imperfect CSI can achieve the same Doppler diversity order as those with perfect CSI, but there is always a non-diminishing SNR gap between systems with and without perfect CSI. The maximum Doppler diversity can be obtained in practical systems by designing various linear precoders and optimum receivers that take into considerations of channel estimation errors.

4.1.5 PRACH Enhancement Nowadays, the designed highest speed train could be beyond 400km/h. The higher and higher speed brings challenges to the current LTE mobile communication system which is because that higher speed will cause higher Doppler frequency shifts to the wireless signal over the air, and the higher Doppler frequency shifts will result in system performance degradation. Furthermore, assuming the frequency shift of the downlink caused by Doppler frequency is 𝑓𝑑 , the frequency shifts caused by the UE speed in the uplink will be two times of the Doppler shifts in the downlink due to that the UE will synchronize on an frequency that the carrier plus the Doppler frequency and transmit uplink signal at the synchronized frequency, and an extra Doppler frequency will be added to the uplink signal received by the base station. One of the functions impacted is the random access. In the current LTE system, the random access is design to cope with the Doppler frequency shift no larger than the preamble signal subcarrier spacing. But with higher speed or higher carrier frequency, the Doppler frequency shifts will be larger than one subcarrier spacing of the preamble signal.

Doppler frequency shift (times of preamble subcarrier spacing)

2.5 2 1.5

300Km/h 350Km/h

1

400Km/h

0.5 0 2.0GHz

2.6GHz

3.5GHz

Fig. 4-13 Doppler frequency shift with different speed and carrier frequency. The above figure shows the uplink Doppler frequency shifts along with the carrier frequency and the terminal moving speed. For example for carrier 2.6GHz, speed of 350km/h and 400km/h will result in the Doppler frequency of 1685 and 1925 Hz at the eNB side which is 1.34 and 1.54 times of the preamble subcarrier spacing (1250Hz). The Doppler 23

5G White Paper：Key Technologies and Solutions for 5G HSR frequency shifts @2.6GHz with speed beyond 350km/h will exceed the design of the LTE system for PRACH capability. When higher speed (above 350 Km/h) or higher carrier frequency (band 22/42/43) is applied, the timing and frequency offset ambiguity still exists, which will degrade the uplink random access performance (higher random access failure rate, higher false alarm rate and increased random access latency). In the following, we give our simulating result with the assumptions u  5(index  265), d u  168, N cs  100 . From the simulating result, we can observe that there will be five correlation peaks while the Doppler shift beyond the range of [-1.25KHz, +1.25KHz].And the difference between Fig.4-14 and Fig.4-15 is that in Fig.4-14 the great peak distributes at 2d u while the Doppler shift is 1.6f and the great peak distributes at 2du while the Doppler shift is 1.6f in Fig.4-15. 5

4.5

x 10

4

3.5

3

2.5

2

1.5

1

0.5

0

0

100

200

300

400

500

600

700

800

900

Fig.4-14 u  5(index  265), d u  168, N cs  100, f d  1.6f 5

4.5

x 10

4

3.5

3

2.5

2

1.5

1

0.5

0

0

100

200

300

400

500

600

700

800

900

Fig.4-15 u  5(index  265), d u  168, N cs  100, f d  1.6f To avoid the interference to other UEs or eliminated the detection ambiguity, the cyclic shift zone corresponding to five correlation peaks should be reserved to one UE. So, one of the feasible solutions is to develop new cyclic shift restriction set. The cyclic shifts

Cv

of the new restricted set [19-20] are given by 24


 





RA RA d start v n shift  v mod nshift N CS  Cv  d start  v  wN CS  RA d start  v  w  nshift N CS 



w

du

RA RA n shift n group

v  0,1,..., w  1 v



v

RA w,..., w  nshift

RA RA w  nshift ,..., w  nshift

for new restricted sets 1 

RA nshift

for new restricted sets  1 for new restricted sets

RA  n shift

is the cyclic shift corresponding to a Doppler shift of magnitude of PRACH subcarrier spacing of 1.25KHz and

given by 0  p  N ZC 2 p du    N ZC  p otherwise

where p is the smallest non-negative integer that fulfils

 p  u  mod N ZC  1

for the u

th

root Zadoff-Chu

sequence. For

N CS  d u  N ZC / 5

, the parameters

RA RA RA nshift , d start , ngroup , nshift

are derived as follows:

RA nshift  d u N CS  RA d start  4d u  nshift N CS RA ngroup  N ZC d start 



RA RA nshift  max ( N ZC  4d u  ngroup d start ) N CS ,0

For

N ZC / 5  d u  ( N ZC  N CS ) / 4



RA RA RA nshift , d start , ngroup , nshift are derived as follows::

, the parameters

RA nshift  ( N ZC  4d u ) N CS  RA d start  N ZC  4d u  nshift N CS RA ngroup  d u d start 







RA RA RA nshift  min max (d u  ngroup d start ) N CS ,0 , nshift



N ZC  N CS N ZC  N CS 2 2  d u  N ZC  d u  N ZC n RA d n RA n RA d 4 7 4 7 For or , the parameters shift , start , group , shift , start , RA nshift

,

RA d start nshift


 4d  N ZC  RA nshift  u   N CS  RA d start  4d u  N ZC  nshift  N CS

 d  RA n group  u   d start  RA  N ZC  3d u  n group  d start RA nshift  max(  N CS 

 

 ,0) 



RA RA RA nshift  min d u  n group  d start , 4d u  N ZC  nshift N CS / N CS RA RA d start  N ZC  3d u  n group  d start  nshift N CS



















RA RA RA RA RA RA nshift  1  min 1, nshift d u  n group  d start  min 1, nshift 4d u  N ZC  nshift N CS / N CS  nshift RA RA d start  N ZC  2d u  n group  d start  nshift N CS

25


N  N CS 2 N  N CS 2 N ZC  d u  ZC N ZC  d u  ZC n RA n RA n RA d 3 3 For 7 or 7 , the parameters shift , start , group , shift ,

d start

,

RA nshift

,

RA d start nshift


 N  3d u  RA nshift   ZC   N CS  RA d start  N ZC  3d u  nshift  N CS

 d  RA n group  u   d start  RA  4d u  N ZC  n group  d start  RA nshift  max(   , 0) N CS  

 



RA RA RA nshift  min d u  n group  d start , N ZC  3d u  nshift N CS / N CS



RA RA d start  d u  n group  d start  nshift N CS RA nshift 0

d start  0

N ZC  N CS 2 N ZC N ZC  N CS 2 N ZC  du   du  n RA n RA n RA d 3 5 3 5 For or , the parameters shift , start , group , shift ,

d start

,

RA nshift

,

RA d start nshift


 3d  N ZC  RA nshift  u   N CS  RA d start  3d u  N ZC  nshift  N CS

 d  RA n group  u   d start  RA  N ZC  2d u  n group  d start  RA nshift  max(  ,0) N CS   RA nshift 0

d start  0 RA nshift 0

d start  0

2 N ZC N  N CS 2 N ZC N  N CS  d u  ZC  d u  ZC n RA n RA n RA d 5 2 5 2 For or , the parameters shift , start , group , shift ,

d start

,

RA nshift

,

RA d start nshift


26


 N  2d u  RA nshift   ZC   N CS  RA d start  2( N ZC  2d u )  nshift  N CS

 N  du  RA n group   ZC   d start  RA  3d u  N ZC  n group  d start  RA nshift  max(  ,0) N CS   RA nshift 0

d start  0 RA nshift 0

d start  0 To ensure the equations can describe all the preamble which can avoid interference among UEs, the evaluation is performed. The number of preambles derived by computer searching is taken as comparison. In the table 4-5, comparison of number of preambles derived from new restricted set and computer searching are provided. From the simulation results, it can be observed that new restricted set covers all preambles supporting [-2.5 KHz, 2.5 KHz].

Table 4-5 Comparison of number of preambles derived from new restricted set and computer searching Index

N CS

Number of

Number of

preambles derived

preambles derived

from new restricted

by computer

restricted Set 3 ( M 2new ) .5 KHz

searching

Coverage of new restricted Set

=

restricted M 2new / M 2All .5 KHz .5 KHz  100 %

All

( M 2.5 KHz ) 0

15

6230

6230

100%

1

18

4960

4960

100%

2

22

3888

3888

100%

3

26

3112

3112

100%

4

32

2382

2382

100%

5

38

1842

1842

100%

6

46

1390

1390

100%

7

55

1040

1040

100%

8

68

722

722

100%

9

82

430

430

100%

10

100

308

308

100%

11

128

166

166

100%

12

158

42

42

100%

13

202

0

0

100%

14

237

0

0

100%

15

-

-

-

-

27


4.2 Handover Oriented Key Technologies and Solutions for 5G HSR 4.2.1 UE Mobility Identity-based Handover Solution To guarantee the quality of service for users in high speed trains, operators have deployed the so-called “high-speed-railway dedicated network”, referred to as “dedicated network” hereafter. Compared to the network deployed for the users out of high speed trains with normal-medium mobility, referred to as “public network” hereafter, the dedicated network exploits specific design to provide better coverage along the high speed railways. At present, in some areas handover is allowed between the two types of networks, which makes the users out of / in high speed trains may camp on the dedicated network / public network. The users out of high speed trains camping on the dedicated network have frequently caused congestion of the dedicated network and thus affected the quality of service for users in high speed trains. At the same time, the users in high speed trains camping on the public network are susceptible to handover failure because of more frequent handover between public cells. In order to solve these problems, we need to study how to avoid the users out of / in high speed trains camping on the dedicated network / public network. In the following, we give an UE mobility identity-based handover solution for the above problems. In this solution, UE mobility identity means that the UE is in or out of high speed train. The UE in high speed train is named “high speed UE” and should be connected to the dedicated network, while the UE out of high speed train is named “normal UE” and should be connected to public network. Firstly, the mobility identity of the UE is determined and stored when it enters RRC_CONNECTED. In the subsequent handover procedure, the base station makes handover decision using the mobility identity of the UE. 

UE mobility identity determination.

In LTE, mobility state detection is supported for the UE in RRC_CONNECTED. According to the number of cell reselections during a certain time period, the mobility state of UEs can be clarified into three types, i.e., normal-mobility state, high-mobility state and medium-mobility state. However, the results of the present mobility state detection can only represent the relative speed of UEs, and cannot be applied in our scenario where there is much difference in the aspect of coverage characteristics between the cells of dedicated and public network. In this solution, we propose two methods of estimating UE mobility to distinguish the UE mobility identity. –

Option A: Improve the present mobility state detection mechanism in LTE using the difference of coverage characteristics between the cells of dedicated and public network.

–

Option B: Set a timer in the dedicated cell to monitor whether the time when the UE camps on it exceeds the predetermined threshold

In Option A, we assign different weights to the cells of dedicated and public network, according to the difference of coverage characteristics (e.g. field strength, shape, and size) between the cells of dedicated and public network. Compared to the mobility state detection mechanism in LTE, in our solution the type (i.e. dedicated/public) of the cells is recorded and the number of cell reselections is counted with the weights of the cells of dedicated and public network. In Option B, we set a threshold for the mentioned timer in each dedicated cell, according to the coverage characteristics (e.g. shape and size) of the cell as well as the speed of the train. The UE is regarded as a high speed UE if all of the timers in the N cells which the UE has connected to are less than the corresponding thresholds. Otherwise, the UE is regarded as a normal UE. 28

5G White Paper：Key Technologies and Solutions for 5G HSR 

UE mobility identity-based handover

The determined UE mobility identity is stored to improve the handover. Based on the UE mobility identity, the normal/ high speed UE in a dedicated/public cell will be switched to a public / dedicated cell and the source base station will transfer the UE mobility identity to the target base station if the handover condition is met. The below Fig.4-16 depicts the proposed handover procedure for the normal UE in a dedicated cell.

Normal UE

Source

Target

Dedicated base state

Public base state

1. Measurement Control

UL allocation

2.

Measurement Reports （including UE mobility idenity） 3. Handover decesion (Adapt the target BS to the UE mobility identity) Handover Request 4. （including UE mobility idenity） 5. Admission control 6. Handover Request Ack DL allocation

RRC Conn. Reconf. incl. 7. mobilityControlinformation

Fig.4-16 UE Mobility Identity-based Handover Procedure. If the handover condition is not met, release the connection after the current service is over, redirect the normal / high speed UE to a public / dedicated cell, and report the mobility identity information by the UE to the new base station. In the subsequent handover, apart from the present measurement report and RRM information, the source base station takes the UE mobility identity into consideration for handover decision in order to avoid the normal / high speed UE connect to a dedicated/ public cell again, and the source base station keep to transfer the UE mobility identity to the target base station. The above UE mobility identity-based handover solution ensures that the UE connect to a cell that adapts to its mobility identity, which improves the user experience of high speed UE.

4.2.2 Power Adjustment Assisted Handover In future 5G communication systems, supporting high-quality wireless communications in high-speed railway scenarios is of high importance. In high-speed railway system, the radio condition or received signal strength (RSS) plays a very important role especially for handover. One promising method is to increase transmit power to improve the RSS to reduce the handover failure probability, it goes against the requirement of green communications in 5G communication systems due to more energy consumption. To improve handover performance without increasing total energy consumption, a novel handover design with smart power adjustment is presented, which is proposed from the perspective of reducing the “uncertainty” in handovers. The main idea of the proposed design is illustrated in Fig. 4-17.

29


(a)

(b)

(c)

Fig. 4-17(a) No-fading case. (b) Traditional case. (c) Proposed case. As shown in Fig. 4-17(a), if there is no fading, the RSS from the two neighboring cells should be determined by the pathloss as shown where the handover is triggered in a certain position. However, in practical, the impact of the fading on the RSS makes the RSS vary dramatically as shown in Fig. 4-17(b). As a result, the handover may be triggered “uncertainly”, which may frequently cause handover failure. Therefore, the main idea of the proposed design is to reduce the “uncertainty” in the handover so that the RSS can be more distinguishable as shown in Fig. 4-17(c).

Fig. 4-18 Illustration of the proposed handover design within the overlap region. The overlap region can be divided into two subregions as shown in Fig. 4-18. The first subregion is from A to B and the second subregion is from B to C, where A and C are the starting point and the ending point of the overlap region, respectively, and B is the middle point of two neighboring cells. The proposed power adjustment is described as follows. 1)

Within the first subregion (A, B), the train is closer to the serving cell and farther away from the target cell, so the pathloss from the target cell remain serious, which means that the RSS from the target cell lower than the minimum service required RSS is true with high probability. In this case, we should try the best to avoid the occurrence of handover caused by a sudden fading on the RSS from the serving cell. To this end, within this subregion, we need to increase the transmit power of the serving cell and decrease the transmit power of the target.

2)

Within the second subregion (B, C), the train is closer to the target cell and farther away from the serving cell, so the pathloss from the serving cell to the train is more serious, which means that handover condition is true with high probability. In this case, we should try the best to guarantee the RSS from the target cell greater than the minimum service required RSS. To this end, we increase the transmit power of the target cell and decrease the transmit power of the serving cell.

30

5G White Paper：Key Technologies and Solutions for 5G HSR With our proposed power adjustment, within the first subregion (A, B), the handover failure probability can be reduced by not letting the handover be triggered, and within the second subregion (B,C), the handover failure probability can be reduced by avoiding the communication interruption when handover is triggered. As a result, the handover is likely triggered in the second subregion (B,C), where the train is closer to the target cell and the RSS is expected to be better than that from the serving cell due to the smaller pathloss, so the handover failure probability could be reduced. To show the efficiency, the proposed design is applied in a high-speed railway scenario with distributed antenna system (DAS) cells as shown in Fig. 4-19(a), where both the blanket transmission based handover scheme and the RAU selection transmission based handover scheme are discussed.

(a)

(b)

Fig. 4-19 (a) High-speed train in DAS cell. (b) The proposed handover procedure.

Fig. 4-20 The handover failure occurrence probability within the overlap region. It can be seen in Fig. 4-20 that the proposed scheme can reduce the energy consumption without degrading the system performance, which meets the requirement of green communication. Besides, it can be seen that the RAU selection transmission based handover scheme is able to achieve lower handover failure probability compared with the blanket transmission based handover scheme. This is mainly because the former one can provide the RSS with higher quality, however, such a transmission scheme is more complicated by selecting the RAU for the train according to the position of the train.

31


4.3 Coverage Oriented Key Technologies and Solutions for 5G HSR 4.3.1 Time-domain Power Allocation for Enhancing Near-far Coverage How to provide efficient information service for high-mobility scenarios, including high-speed railway communications (HSRCs), is one of the most important requirements in future 5G communication networks. In HSRCs, due to pass-loss effect, the information rate between roadside base station (BS) and the moving train closely depends on the distance between the BS and the train, and the high-mobility speed of trains makes the path loss vary fast with time as shown in Fig. 4-21. It is essential to implement time-domain power allocation to mitigate the near far effect. To explore the information transmission capacity of HSRCs, some power allocation schemes were developed and proved to be efficient.

Fig. 4-21 Illustration of HSRC system. With the water-filling method, the maximum amount of information (mobile service amount) can be delivered, however, most power is allocated to the time interval when the train is nearest to the BS, which causes great unfairness with respect to time. Although the proportional power allocation can achieve much better fairness along the time, it causes a relatively big loss in mobile service amount, resulting in low utilization efficiency of HSRC channels. Enlightened by the dentition of RKenyi entropy, a novel power allocation scheme called -fairness power allocation is presented, which is able to achieve relatively high mobile service amount with fairness between the water-filling and proportional power allocation. It is a generalized fairness power allocation, by which the tradeoff between the mobile service amount and fairness can be easily controlled by adjusting the value of  . The  -fairness power allocation can be expressed by

(4-12) where

with   0 . Particularly, as  =0 , it becomes a traditional water-filling method, while  =1 , it becomes the existing proportional power allocation. To achieve a more general power adjustment with user QoS requirement, the rate-constrained  -fairness power allocation can be applied, where the minimal rate of the train can be guaranteed while being aware of the time fairness. 32

5G White Paper：Key Technologies and Solutions for 5G HSR Fig. 4-22 shows the mobile service amount obtained by different schemes, i.e. different values of  . Fig. 4-23 shows the instantaneous achievable information rate obtained by different schemes, i.e. different values of  . Fig. 4-24 shows the instantaneous achievable information rate under the minimal information rate constraint, obtained by different schemes, i.e. different values of  .

Fig. 4-22 Comparison of mobility service amount versus time  , with the transmit power being 5 dBW, the cell radius being 2.5 km and  =0.25 and  =0.55 .

Fig. 4-23 Comparison of instantaneous achievable information rate versus time  , with the transmit power being 5 dBW, the cell radius being 2.5 km and  =0.25 and  =0.55 .

33

5G White Paper：Key Technologies and Solutions for 5G HSR Fig. 4-24 Comparison of instantaneous achievable information rate versus time  under the minimal information rate constraint, with the transmit power being 5 dBW, the cell radius being 2.5 km and  =0.25 and

 =0.55 .

4.4 Capacity Oriented Key Technologies and Solutions for 5G HSR 4.4.1 Fixed Beam Direction based Transmission A promising deployment of the gNB is that the placement along the rail track, where remote radio heads (RRHs) connected to the centralized baseband unit (BBU) pool are deployed along the railway as shown in Fig.4-25,. Each RRH is equipped with a panel of an antenna array which can create a directional narrow analog beam along the railway track. At the same time, another antenna panel is equipped on top of a train (at the end and/or head of each train), generating a directional beam towards the RRH. The short distance between the RRH and railway track is assumed to be around 5m. The in-train access points (APs) are connected to the UE deployed at the head of the train and relay user data streams between the RRHs and UEs. The link between the AP and UEs can be established by WiFi or MiFi which typically uses below 6 GHz band. Inside the train carriage, the channel characteristics are similar to those of an indoor environment, and at the same time, the link between the RRH and the UE can use high-freq or low-freq which may have a different frequency band with the in-train communication system. Hence, the out-train data transmission can be considered independently.

Fig. 4-25 A typical example of Fixed Beam Direction based Transmission [21].

4.4.2 Location-aided MIMO Transmission In MIMO systems, to improve the capacity, the instantaneous channel state information is usually required. Due to the high moving speed, the coherence time of the channel can be less than 1 ms, and hence the CSI estimation feedback at the BS is usually outdated. If the outdated channel estimation is directly used for MIMO precoding design, the capacity would be greatly reduced by the channel estimation error. However, the HSR routes are mainly composed of viaduct and wide plain, which yields the HSR channel composed of a strong LOS path with few NLOS path cause by trees or other reflectors and scatters. The accuracy of channel estimation can be improved via modifying the outdated channel based on the position information of the train. Actually, the position of antenna arrays at the BS can also be used to reduce the training overhead during the channel estimation. Only a subset of the BS antennas needs to send RS, when the BS obtains the channel between the UE and the partial antennas, the entire channel matrix could be constructed based on the partial channel and the position of the antenna array by exploiting the spatial-temporal correlation of the channel. 34


The location-aided umbrella-shaped transmission schemes [22] needs neither uplink channel covariance matrix (UCCM) nor downlink CCM (DCCM) but precalculates the beamforming weights with the help of train location information, which can be completed through pure off-line calculation and therefore reduce system implementation complexity. An example of the scheme is given in Fig.4-26, where the BS is equipped with multiple single-array M-elements ULA antennas, and the mobile receiver is mounted on the top of each carriage. Different from the traditional SFBC/STBC transmission scheme, this scheme utilize the location of the train to design the direction of each beam. Once the BS is deployed, the beams directed to different locations are generated according to the angle of departure (AoD) information, and this AoD information can be pre-store off-line and when the train comes, the BS can choose the right beam direction according to the train location.

Fig. 4-26 An example of location--aided umbrella-shaped schemes [22].

4.4.3 Space-Time-Coded Adapticve Spatial Modulation for HSR When some channel conditions are particularly poor, it is not necessary to select this antenna to transmit the signal. Recent work on spatial modulation includes generalized spatial modulation (GSM) and adaptive spatial modulation (ASM). Compared to the conventional spatial modulation (SM), two antennas are activated simultaneously to transmit information symbols in GSM, instead of using one antenna each time slot. The data rate of GSM system is larger than SM when they have equal number of transmit antennas. ASM scheme was proposed in [23], in which different levels of modulation schemes are selected adaptively based on a low-complexity modulation order selection criterion (MOSC) for different channel conditions. Thus, this scheme achieves better error performance with the same data rate target compared to the conventional SM. Inspired by these points, we propose an adaptive spatial modulation (ASM) scheme embedded Alamouti space-time block coding (STBC) in the high speed communication system. Instead of selecting one active antenna in each transmission in conventional ASM, we activate two antennas as one antenna group simultaneously to encode the information bits into Alamouti code blocks, each of which needs two channels used. In the ST-ASM system, two antennas are activated in each transmission and Alamouti code blocks obtained by encoding information symbols are transmitted from the active antennas. Compared to the conventional SM and ASM, the proposed ST-ASM scheme provides larger diversity gain and better error performance when using equal data rate and equal transmission power [24]. The block diagram of ST-ASM transceiver is shown in Fig. 4-27. Specifically, it is realized by selecting two active antennas simultaneously. Then, Alamouti code is used here and the code blocks are 35

5G White Paper：Key Technologies and Solutions for 5G HSR transmitted through the two selected antennas. Similar to ASM scheme, the modulation orders (MO) assigned to each transmit antenna are chosen adaptively according to different channel conditions.

Fig. 4-27 The block diagram of ST-ASM transceiver. It is worth mentioning that there exists zero-order modulation for antenna group in the process of MO selection, in which no information bits are modulated at transmit antennas. It is a waste of antenna and higher MO should be selected for other antenna groups to guarantee target system data rate. It suggests that zero-order modulation should be removed in the process of MO selection. We first consider the maximum likelihood (ML) detection of ST-ASM. Assume that the channel state information (CSI) is perfectly known at the receiver. Then, we get the joint ML estimation as

(lˆg ,qî ,qî+1 )  arg ˆ min y  H lg xqî ,qî+1 lg ,qî ,qî+1

where y is the received signal vector, estimated symbols,

2 F

2 F

(4-13)

lˆg is the index of the estimated 2 antenna group, qî and qî+1 are the

represents the Frobenius norm of a vector. Since there is no closed form expression of the bit

error rate (BER) of eq. (4-13), we use the nearest neighborhood approximation as its performance metric. Given the channel matrix H, the nearest neighborhood approximation of the pairwise error probability (PEP) can be expressed as

 1 2  1 Pe   exp   d min ( H )  2  4 N0  where



is the average number of neighbors,

2 d min (H )

(4-14)

represents the received minimum distance which is

defined as 2 d min ( H )  min H  xt -xt+1  xt ,xt 1

2 F

(4-15)

where Φ is the set of all possible transmit symbol vectors, xt and xt+1 are the two successive signal vectors as the output of the Alamouti coding. In another way, we consider another detection scheme, which is an adaptive metric of ST-ASM. At the transmitter of ST-ASM, the bits mapped to spatial constellation select the antenna group, and the modulation order on different antenna is selected adaptively according to the feedback information from the receiver. Similar to the popular ML detector, the adaptively metric is selected to maximize the received minimum distance 2 d min (H ) .

36


Fig. 4-28 BER performance of ST-ASM. Fig. 4-28 shows that the ST-ASM provides the best system performance among three spatial modulation schemes and -3

the ST-ASM outperforms ASM by around 2 dB at Pe=10 . It also observes that the BER performance of the ST-ASM at low SNR is obviously better than the SM and the ASM under wireless fading channels. In a nutshell, the proposed ST-ASM scheme outperforms both conventional SM and ASM.

4.5 Energy Efficient Oriented Key Technologies and Solutions for 5G HSR 4.5.1 Train Arrival Information based Energy Utilization Control Energy saving management refers to optimize the resource utilization efficiency of the entire or part of the network to achieve energy saving through closing certain cells or limiting the resource usage, on the premise that the service requirements are ensured. In the application of high speed railways mobile communication, though it is necessary for the operator to ensure the access of both the users in and out of trains, the service quality of the UE in the train (simply called “high speed UE”) will be impacted, e.g. drop call, if some UEs out of the train (simply called “normal UE”) access the “High-speed-railway dedicated network” (simply called “dedicated network”) for the high speed UE. This impact can be eliminated though preventing normal UE accessing the dedicated network by access control. However, this solution will also make the wireless resource of the dedicated network not able to be utilized before the high speed UEs arrive at the coverage of the dedicated network, which lowers the energy utilization efficiency. At the same time, the nearby public base station may suffer overload. In this case, we need to study how to improve the energy utilization efficiency in the application of high speed railways mobile communication. In the following, we give a train arrival information based energy utilization control mechanism. The main idea is to determine whether close the dedicated cell and extend the coverage of the nearby public cell based on the arrival state 37

5G White Paper：Key Technologies and Solutions for 5G HSR of the train for the dedicated cell, where the arrival state of the train for a dedicated cell refers to the time required by any high speed UE to reach the dedicated cell. The basic procedure is depicted in the figure below.

Dedicated cell#n

Yes Does any high speed UE access To the dedicated cell?

1. The time required by any high speed UE to reach the dedicated cell is set to 0 2. transfers the high speed UE occupation information

No the duration exceeds the first threshold

Estimate the time required by any high speed UE to reach the dedicated cell

confirm that the train has left out of the coverage of the dedicated cell.

exceeds the second threshold

less than or equal to the second threshold

remove the stored arrival state information of the left train to make room for the next train. For the running dedicated cell: close it and extend the coverage of the nearby public cells

For the closed dedicated cell: restart it and resume the coverage of the nearby public cells

Fig. 4-29 Train arrival information based energy utilization control procedure. 

Estimate the train arrival state for each dedicated cell.

The dedicated cell to which at least one high speed UE accesses transfers the high speed UE occupation information, e.g., the identities and positions of the dedicated cell, to dedicated cells that lie along the railways with the direction of the train. The high speed UE occupation information is used to estimate the identity of the dedicated cell to which the high speed UE closest to the edge of the train head accesses. The arrival state of the train for a dedicated cell is calculated in the following ways. –

For the dedicated cell to which at least one high speed UE accesses, the time required by any high speed UE to reach the dedicated cell is naturally set to 0 second.

–

For the dedicated cell to which no high speed UE has accessed, the time required by any high speed UE to reach the dedicated cell is estimated using the train speed and the identity of the dedicated cell to which the high speed UE closest to the edge of the train head accesses.

For the dedicated cell to which no high speed UE accesses, if the duration when no high speed UE accesses to the dedicated cell exceeds the first threshold, we confirm that the train has left out of the coverage of the dedicated cell. In this case, we should remove the stored arrival state information of the left train to make room for the next train. The 38

5G White Paper：Key Technologies and Solutions for 5G HSR first threshold is determined according to the train speed and the coverage characteristics of the dedicated cell and its nearby dedicated cells. 

The dedicated cell state determination

According to the above train arrival state for a dedicated cell, determine whether to close the dedicated cell and extend the coverage of the nearby public cells –

For each dedicated cell that is running, close it and extend the coverage of the nearby public cells if the time required by any high speed UE to reach the dedicated cell exceeds the second threshold (the train is leaving).

–

For each dedicated cell that has been closed, restart it and resume the coverage of the nearby public cells if the time required by any high speed UE to reach the dedicated cell is less than or equal to the second threshold (the train is coming).

Considering it is hard to avoid the error in estimating the time required by any high speed UE to reach the dedicated cell in practical use, the second threshold is calculated based on the time required to start the dedicated cell and the accuracy of the estimation on the time required by any high speed UE to reach the dedicated cell. The above train arrival information based energy utilization control solution saves the energy consumption of the dedicated network when ensuring the service requirements of high speed UEs.

5. Summary The next generation mobile network needs to provide diversified services and satisfy extremely user experience. The HSR is a typical scenario for 5G in China. This white paper aims to conclude the main issues identified by the current network testing from the industrial perspective and provide the potential key technologies and solutions to the discovered problems. In addition to the solutions listed in the main issues part, the new potential solutions are summarized in the below table. Table 5-1: Summary of the solutions

Network Architecture – Moving network: treat the mobile terminals distributed within a train cartage or even the whole train as a whole Key Technologies and Solutions for the Main Issues High Mobility –

New numerology design

(larger

sub-carrier spacing,

flexible

CP config) –

Waveform

(DL:

OFDM,

UL:

OFDM

+

Handover

Coverage

Capacity

– UE mobility identity-based handover (classify different UE mobility state)

– Coverage enhancemen t by time domain power allocation to guarantee the cell-edge

– MIMO transmission scheme enhancement, e.g. Fixed based direction based BF, location-based MIMO,

– Power

39

High Energy Efficient – Train arrival information based energy utilization control


adjustment assisted handover

DFT-s-OFDM, transformed domain

user performance

waveforms) –

space-time coded adaptive spatial modulation and etc.

Channel estimation

and

frequency compensation enhancement –

DM-RS (front

design loaded

+

additional DM-RS,

flesible

config) –

PRACH enhancements (new cyclic shift compatible sets)

Compared with the 1st edition of the high mobility white paper, this white paper focus on the 5G HSR scenario. And some new key technologies and solutions are captured, where some of them had been discussed and agreed in the 3GPP NR meeting. Due to limited length, the key technologies and solutions that are not included in this white paper are not excluded for 5G HSR. This white paper just want to capture some new technologies, potential solutions and the progress in 3GPP NR standardization.

6. Appendix I: High Speed Channel Model A1 High speed railway channel model at Low Frequency Bands A1.1 Path Loss The Hata model has been widely used in the high speed railway (HSR) engineering implementations, therefore, we develop our standard path loss model based on the Hata's formula and a large body of measurements at 930 MHz. The Hata path loss model in urban is considered as the basic formula and the correction factors are added to lead to the models in suburban and open area. The standard Hata model in urban (with the large city correction factor) is as follow

40


PLHata  74.52  26.16 log10 ( f )  13.82 log10 (hb )  3.2 l  log10 (11.75hm ) 

2

(A1-1)

 [44.9  6.55log10 (hb )]log10 ( d ) where f is the carrier frequency in MHz. hb and hm are the BS effective antenna height and the vehicular station antenna height (against the surface of rail track in the HSR) in meters. d is the T-R separation distance in kilometers. The modified path loss model at 930 MHz in HSR based on the Hata's formula is expressed as [25]

PLProposed  1  74.52  26.16 log10 ( f  930)  13.82 log10 (hb )  3.2 l  log10 (11.75hm ) 

2

(A1-2)

 [44.9  6.55log10 (hb )   2 ]log10 ( d ) where Delta1 and Delta2 are the correction factors for the proposed model. The correction factor Delta is derived from the difference between the optimal path loss curve, i.e., the Least-Square (LS) regression fit curve. According to a visual inspection, we find that Delta can be modeled as a function of the logarithmical hb, expressed as [25]

 i  p  log10 (hb )  q

(A1-3)

where p and q are obtained by the LS fit. This expression is also consistent with the formula in Hata model. We enforce p=0 if no distinct linearly decreasing or increasing is observed, and use the averaged value of the measured correction factors as q. Table A1-1 Standardized path loss model (in dB) for HSR environments [25]

The above table summarizes the estimated correlation factors for each scenario based on the LS regression fit. To remove the effect of the BS antenna pattern on the measured path loss, a calibration was conducted. As shown in the table, the derived correlation factors have the similar terms to the Hata formula, and can be easily extended into the Hata model.

A1.2 Amplitude Distribution of Small-Scale Fading 41


High-speed railway propagation scenarios are divided into two regions: Region 1-inside the bottom area of the antenna; and Region 2-outside the bottom area of the antenna. After removing the path loss and large-scale fading from the raw data, we investigate the small-scale fading behavior in Regions 1 and 2. We first examine the empirical distribution of the fading amplitudes. Four distributions, namely, Ricean, Nakagami, Rayleigh, and lognormal are tested using the Akaike’s Information Criteria (AIC).

Fig. A1-1 Relative frequencies of AIC and KS tests selecting a candidate distribution as best fit to small-scale fading amplitudes.

Fig. A1-1 (a) and (b) shows the relative frequency of AIC selecting each of the candidate distributions as best fit. It is found that the Ricean distribution provides the best fit in a majority of the cases. Then, the Kolmogorov–Smirnov (KS) passing rate of each distribution is recorded as a measure of the goodness of fit, as shown in Fig. A1-1 (c) and (d). It is found that the passing rate of the Ricean distribution is generally larger than 80%, which verifies that it offers a satisfactory fit. We henceforth suggest the Ricean distribution in HSR environments.

A2

5G

mmWave

High-Speed

Channel

Characterization

and

Ray-Tracing A2.1 Scenario Modules of 5G mmWave High-Speed Train Channels In order to verify new communication regimes in railway environments, it is critical to define 5G mmWave HSR scenario modules with distinguished propagation features. As shown in Fig. A2-1, six modules respectively on behalf of various typical railway scenarios are constructed. It is noteworthy that the length of these modules are from 100 m to 200 m, which is much shorter than the macro-cell or micro-cell with the carrier frequencies lower than 6 GHz. The high path loss of mmWave band considerably shortens the link length, which gives us the chance to concentrate the totally six modules within 1 km range to compose a comprehensive rail traffic environment. As summarized in Table A2-1, apart from common objects, such as trains, tracks, pylons, traffic signs, billboards, etc., 42


each module includes its special objects, i.e., steep walls, cutting walls, crossing bridges, train stations, indicators, barriers, vegetation, cut and cover tunnels (CCTs), dual-track tunnel, and single-track viaduct. The 3D models of both the comprehensive scenario and the six modules are publicly available and freely downloadable with the link (http://raytracer.cloud).

Fig. A2-1 Panorama and each scenario module of 5G mmWave HSR channels

Table A2-1 Definition of the six scenario modules for 5G mmWave HSR channels

Module

Module 1

Module 2

Module 3

Module 4

Module 5

Module 6

Tunnel

Viaduct

Urban with

Rural with

Rural

Single-track

entrance

with open

semi-closed

cut and

connecting

viaduct

on steep

train

train station

cover

double-track

wall

station

tunnel

tunnel

Index Definition

connecting cutting with crossing bridges 43


Special

Steep wall,

Open train

Buildings,

Vegetation,

Dual-track

Single-track

objects

cutting

station,

semi-closed

CCTs

tunnel,

viaduct,

walls,

indicators

station

barriers

barriers

crossing bridges Common

Trains (metal), tracks (concrete), pylons (metal), traffic signs (metal), billboards

objects

(metal/LED/concrete)

Module 1 – Tunnel entrance on steep wall connecting cutting with crossing bridges Tunnel is an artificial underground passage, especially one built through a mountain in HSR environment. Thus, when the mountain is high usually a steep wall will be constructed in order to protect the tunnel entrance. Such steep wall is a huge reflector for wave propagation. Cutting is another common scenario in HSR. A rail cutting is a man-made valley that carriers the track as its base, which is used to pass through the hill not particularly high. The two cuttings are made of concrete and stone with some vegetation on the surface. The depth of cutting is usually 3-10 m. The slope height is about 13-14 m, and the inclination angle is 35-40 degrees. The distance between two bottoms of slopes is 12-13 m. The most common semi-closed obstacles in cutting are crossing bridges. They can block line of sight (LOS) and generate strong multipath propagation. Thus, in Module 1, a tunnel entrance on a steep wall is designed to connect cutting with two crossing bridges.

Module 2 – Viaduct with open train station Viaduct is a long bridge across the uneven ground in rural or urban. It is a very common scenario in HSR, which covers almost 70% of Beijing-Shanghai HSR line. It is assumed that the LOS propagation condition can be obtained most of the time. However, the barriers on the both sides of the viaduct as well as some tall objects, such as tall trees or billboards, still have an influence on the propagation. Train station is another kind of important structure along railway. The huge awnings, LED indicators, steel frames beside and above the track, and metallic pylons can block parts of LOS and influence the channel. There are mainly three types of modern train stations (especially high-speed train stations): closed type, semi-closed type and open type. The first type is similar to the typical indoor environment, and the latter two types are more special. Usually, the open stations with relatively smaller sizes appear in rural, and the semi-closed stations with relatively larger sizes are constructed in urban. Thus, this module is composed of an open train station (awnings only covering the platforms, not the rails) and a viaduct in rural, to represent the features of these two structures.

44


Module 3 – Urban with semi-closed train station Urban scenario represents the propagation in urban areas when the train is entering, leaving, or passing the city. Most of the buildings are higher than 10 m and are at least 10 m away from the barriers. In the semi-closed scenario, the huge awnings are usually designed to stop the rain from reaching the passengers and the trains. Thus, the channel will appear strong multipath due to the presence of the semi-closed station, barriers and buildings in this module.

Module 4 – Rural with cut and cover tunnel Rural scenario represents the environment where there is a large range of open area, very few buildings and certain vegetation adjacent to the track. Cut and cover tunnel (CCT) is a method of construction for shallow or short tunnels where a trench is excavated and roofed over with an overhead support system strong enough to carry the load of what is to be built above the tunnel. In HSR, there are two motivations of building CCTs: i) to prevent potential landslides, ii) to cover the rail as a defense against the wind and other unfavorable climate conditions. In fact, CCT is not a complex method in construction, but it creates huge challenges to the wireless planners and system designers for railway communications. Thus, in this module, the CCT, barriers, and the dense vegetation adjacent to the barriers are the main structures of which the influence to channels should be evaluated.

Module 5 – Rural connecting double-track tunnel The cross section of double-track HSR tunnels is usually vaulted or semicircle, with a height of 5-10 m and a width of 10-20 m. Two trains have chances to cross each other inside tunnels, and this effect is worthy to be evaluated in detail. Thus, we design this module to study the channels in rural connecting a double-track tunnel.

Module 6 – Single-track viaduct The difference between two single-track viaducts and one double-track viaduct is whether there are barriers between two tracks. With this module, the influences of barriers and train crossing can be further determined.

A2.2 5G mmWave HSR Channel Characterization Based on the ray-tracing simulation results, all the 5G mmWave HSR channel characteristics at the 60 GHz band with 8 GHz bandwidth in the six scenario modules with two antenna height setups, in terms of path loss exponent, standard deviation of shadow fading, Ricean K-factor, root mean square (RMS) delay spread, coherence bandwidth, azimuth angular spread of arrival (ASA) and departure (ASD), and elevation angular spread of arrival (ESA) and departure (ESD), are summarized by Table A2-2 and Table A2-3. These parameters, reflecting the mmWave railway channel features, can be input into various channel models, such as SCM, SCME, WIM1, 45


WIM2, WIM+, QuaDRiGa), COST2100, and GBSMs. Similar methods of using ray-tracing results to parameterize the urban channel in the 5G mmWave standard channel models can be found in [26]. Table A2-2. 5G mmWave HSR channel characteristics with antenna setup 1: Tx 6 m and Rx 4.5 m

Antenna setup 1: Tx 6 m and Rx 4.5 m Module index

1

2

3

4

5

6

Ref.

LOS/NLOS

LOS

LOS

LOS

LOS

LOS

LOS

Path loss exponent

1.78

1.57

1.88

1.62

1.86

1.69

[27]

Shadow factor [dB]

4.99

5.26

5.33

6.28

5.82

6.08

[27]

Mean value of Ricean

-6.00

-30.91

-38.14

-27.84

-2.10

-7.18

[27]

7.43

31.95

14.90

37.61

1.93

24.45

[27]

74.90

76.80

56.00

31.80

113.00

61.55

[27]

Mean value of ASA [o]

66.71

60.55

53.54

63.75

81.69

76.21

[28]

Mean value of ESA [o]

38.81

28.40

28.61

29.90

40.76

37.74

[28]

Mean value of ASD [o]

79.72

64.20

62.00

65.44

2.26

73.35

[28]

Mean value of ESD [o]

12.02

8.00

9.54

8.82

2.38

4.52

[28]

K-factor [dB] Mean value of RMS delay spread [ns] Mean value of 50% coherence bandwidth [MHz]

Table A2-3. 5G mmWave HSR channel characteristics with antenna setup 2: Tx 1 m and Rx 0.92 m

Antenna setup 2: Tx 1 m and Rx 0.92 m Module index

1

2

3

4

5

6

Ref.

LOS/NLOS

LOS

LOS

LOS

LOS

LOS

NLOS

Path loss exponent

1.78

1.49

1.76

1.49

1.85

2.57

[27]

Shadow factor [dB]

5.34

5.44

5.85

4.96

6.51

10.92

[27]

Mean value of Ricean

-6.54

-19.48

-14.12

-18.32

-5.64

-41.22

[27]

3.97

21.71

12.64

20.26

3.20

7.64

[27]

402.00

71.33

99.75

89.20

406.5

294.35

[27]

K-factor [dB] Mean value of RMS delay spread [ns] Mean value of 50% coherence bandwidth

3

[MHz] Mean value of ASA [o]

84.05

80.12

80.34

74.46

91.33

14.94

[28]

Mean value of ESA [o]

11.32

7.15

8.81

8.65

2.18

3.46

[28]

46


Mean value of ASD [o]

78.27

85.44

102.85

90.10

5.62

13.96

[28]

Mean value of ESD [o]

11.64

5.60

9.65

8.28

1.93

3.04

[28]

Reference [1] R1-165576, “WF on evaluation assumption for high speed train scenario”, 3GPP TSG RAN1 Meeting #85, Nanjing, China, May 23-27, 2016. [2] 3GPP TR 38.913, “Study on Scenarios and Requirements for Next Generation Access Technologies Updated Evaluation Assumptions for NR High Speed Scenario”, 3GPP TSG RAN1 Meeting #84bis, Busan, Korea April 11-15, 2016. [3]

M. Ni, L. Zheng, F. Tong, J. Pan, L. Cai, “A Geometrical-based Throughput Bound Analysis for Device-to-Device Communications in Cellular Networks”, IEEE J. Sel. Areas Commun., 33(1), pp: 100 - 110, 2014.

[4] M. Ni, J. Pan, L. Cai, “Geometrical-Based Throughput Analysis of Device-to-Device Communications in A Sector-Partitioned Cell”, IEEE Trans. Wireless Commun., 14(4), pp: 2232 - 2244, 2015. [5] R1-165371, “Discussion of numerology for NR”, Convida wireless, 3GPP RAN1 #85. [6] R1-167107, “Discussion and Evaluation on Numerology Design for High Speed Train”, CMCC, 3GPP RAN1 #86 [7] A. Goldsmith, “Wireless Communications”. Cambridge, U.K.: Cambridge Univ. Press, 2005. [8] 3GPP TS 38.211 v1.0.0 [9] G. Wang, Q. Liu, R. He, F. Gao, and C. Tellambura,“Acquisition of channel state information in heterogeneous cloud radio access networks: challenges and research directions,” IEEE Wireless Communications Magazine, vol. 22, pp. 100-107, Jun. 2015. [10] X. Wang, G. Wang, J. Sun, and Y. Zou, “Channel estimation with new basis expansion model for wireless communications on high speed railways,” in IEEE 83rd Vehicular Technology Conference, Nanjing, CHN, May 2016, pp. 1-5. [11] C. X. Wang, A. Ghazal, B. Ai, Y. Liu, P. Fan, "Channel measurements and models for high-speed train communication systems: A survey", IEEE Communication Surveys and Tutorials., vol. 18, no. 2, pp. 974-987, 2nd Quart. 2016. [12] 3GPP RAN1 #90bis, Chairman’s Notes Final. [13] X. Ma and G. B. Giannakis, ``Maximum-diversity transmissions over time-selective wireless channels,'' in Proc. IEEEWireless Commun. Netw. Conf. (WCNC), vol. 1. Mar. 2002, pp. 497-501. [14] J. Wu, “WLCp1-04: Exploring maximum Doppler diversity by Doppler domain multiplexing,”in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), Nov./Dec. 2006, pp. 1-5. [15] X. Ma and G. B. Giannakis, ``Maximum-diversity transmissions over doubly selective wireless channels,'' IEEE Trans. Inf. Theory, vol. 49, no. 7, pp. 1832-1840, Jul. 2003. [16] K. Fang and G. Leus, ``Spacetime block coding for doubly-selective channels,'' IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1934-1940, Mar. 2010. [17] M.-A. R. Baissas and A. M. Sayeed, ``Pilot-based estimation of time varying multipath channels for coherent CDMA receivers,'' IEEE Trans. Signal Process., vol. 50, no. 8, pp. 2037-2049, Aug. 2002. 47

5G White Paper：Key Technologies and Solutions for 5G HSR [18] W. Zhou, J. Wu, and P. Fan, `Ènergy and spectral efcient Doppler diversity transmissions in high-mobility systems with imperfect channel estimation,'' EURASIP J. Wireless Commun. Netw., vol 2015, no. 1, pp. 1-12, May 2015. [19] 3GPP TS 36.211 v14.4.0 [20] 3GPP TS 38.211 v1.0.0 [21] S. Choi et al., “Mobile hotspot network system for high-speed railway communication using millimeter waves,” ETRI Journal, vol. 38, no. 6, pp. 1052-1063, Dec. 2016. [22] X. Chen, J. Lu, S. Liu and P. Fan, “Location-Aided Umbrella-Shaped Massive MIMO Beamforming Scheme with Transmit Diversity for High Speed Railway Communications,” IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, China, 15-18, May, 2016. [23] P. Yang and Y. Xiao, “Adaptive spatial modulation for wireless MIMO transmission systems,” IEEE Commun. Lett., vol. 15, no. 6, pp. 602–604, 2011. [24] S. Wang, F. Wang, and Z. Li. "Space-Time-Coded Adaptive Spatial Modulation in Wireless MIMO Communication Systems." Frequenz, vol. 69, no.7-8, pp. 335-339, 2015. [25] R. He, Z. Zhong, B. Ai, and K. Guan, “Reducing cost of the high-speed railway communications: from propagation channel view,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 2050-2060, August 2015. [26] K. Guan et al., “Scenario Modules and Ray-tracing Simulations of Millimeter Wave and Terahertz Channels for Smart Rail Mobility,” 2017 11th European Conference on Antennas and Propagation (EUCAP), Paris, 2017, pp. 113-117. [27] K. Guan, B. Ai, B.L. Peng, D.P. He, B. Hui, J. Kim, and T. Kuerner, “Towards Smart Rail Mobility at mmWave and THz Bands: Challenges, Solutions, and Future Directions,” to appear, IEEE Transactions on Intelligent Transportation Systems, 2018. [28] K. Guan, B. Ai, B.L. Peng, D.P. He, B. Hui, J. Kim, and T. Kuerner, “Towards Smart Rail Mobility at mmWave and THz Bands: Challenges, Solutions, and Future Directions,” to appear, IEEE Transactions on Intelligent Transportation Systems, 2018.

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Abbreviation 3GPP

3rd Generation Partnership Project (3GPP)

5G

The Fifth-Generation mobile communications

APs

Access Points

ASM

Adaptive Spatial Modulation

AWGN

Additive White Gaussian Noise

BBU

BaseBand Unit

BEM

Basis Expansion Model

BER

Bit Error Rate

BS

Base Station

C-BEM

Complex-exponential BEM

CSI

Channel State Information

DFT-S-OFDM

Discrete Fourier Transform Spread OFDM

eMBB

Enhanced Mobile Broadband

eNB

Evolved Node Base station

gNB

next Generation Node Base station

HiBEM

Historical Information based Basis Expansion Model

HST

High Speed Train

HSR

High Speed Railway

ICI

Inter-Carrier Interference

ISD

Inter Site Distance

ITU

International Telecommunications Union

KPI

Key Performance Indicator

LMMSE

Linear Minimum Mean Square Error

LS

Least Square

LTE

Long-Term Evolution

MIMO

Multiple-Input Multiple-Output

mMTC

Massive MTC

ML

Maximum Likelihood 49


NR

New Radio

OFDM

Orthogonal Frequency Division Multiplexing

P-BEM

Polynomial BEM

PEP

Pairwise Error Probability

QoS

Quality of Service

RRU

Radio Remote Unit

RAMS

Reliability, Availability, Maintainability and Safety

SFN

Single Frequency Network

SM

Spatial Modulation

SINR

Signal to Interference and Noise Ratio

STBC

Space-Time Block Coding

TRP

Transmission and Reception Point

TTI

Transmission Time Interval

UE

User Equipment

URLLC

Ultra Reliability Low Latency Communication

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Acknowledgement Grateful thanks to the following contributors for their wonderful work on this white paper:

Editors: CMCC:

Chih-Lin I, WANG Sen

BJTU:

AI Bo

Contributors: CMCC:

WANG Sen, CUI Chunfeng, HAN Shuangfeng, ZHOU Wei, ZUO Jun, XU Guozhen, WANG Ailing

BJTU:

AI Bo, WANG Gongpu, WANG Fanggang, XIONG Ke, NI Minming, GUAN Ke, HE Ruisi

SWJTU:

LIANG Yu, MA Zeng

CATT:

LIU Jun, LI Guoqing

ZTE:

MENG Xi, SUN Ying, ZHOU Dong, ZHU Fusheng, WANG Xinhui

Hua Wei:

ZHOU Liping, CHENG xingqing, ZHAO yue

Spreadtrum:

WANG Hualei, XU Zhikun

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