LTE Physical Layer Identity Detection: Frequency vs Time Domain ...

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

LTE Physical Layer Identity Detection: Frequency vs Time Domain Schemes Hendra Setiawan, Masayuki Kurosaki, Hiroshi Ochi Computer Science and Electronics, Kyushu Institute of Technology Kawazu 680-4, Iizuka-shi, Fukuoka-ken, Japan, 820-8502 Email: [email protected], [email protected], [email protected] TABLE I ROOT INDICES FOR THE PSS

Abstract—Long Term Evolution (LTE) physical layer identity can be recognized either in frequency domain or time domain. In this paper we serve some comparisons in term of complexity, latency, and recognition probabilities in AWGN and EPA channel models. Based on our simulation, time domain scheme gives better detection performance on both channel models than the frequency domain scheme. It also fits the performance requirement in term of receiver minimum sensitivity on AWGN channel. Employing ideal frame synchronization on frequency domain detection could increase the detection performance such that exceeds time domain detection performances. Furthermore, frequency domain detection scheme has shorter latency than time domain scheme even though it requires higher complexity due to FFT involvement.

section III and IV, respectively. The comparisons in term of complexity, latency, and recognition probability are given in section V. Finally, conclusions are drawn in section VI.

I. I NTRODUCTION

II. P HYSICAL L AYER I DENTITIES IN P-SCH S IGNALS

The LTE, also known as 3.9G, is intended to enhance the 3G and 3.5G systems in order for them to adopt higher peak data rates with extreme high mobility support, [1] [2], [3], [4], and [5]. As in conventional cellular systems, just the LTE user equipment (UE) is turned on, UE is searching for the base station with the lowest path loss without having any pre-knowledge of the mobile communication environment [1], and [2]. This process is called ”initial cell search” aimed at frequency and timing synchronization as well as parameters recognition. One of important parameters that has to be recognized during initial cell search is referred as ”physical layer identity”. It is utilized to differentiate between the signals of different radio cells. Based on [6], there are 504 unique physical-layer cell ). The physical layer cell identities are cell identities (NID grouped into 168 unique physical layer cell identity groups (1) (NID ), each group containing three unique identities referred (2) as physical layer identity (NID ). The grouping is such that each physical layer cell identity is part of one and only one physical layer cell identity group. Physical layer identity is recognized by detecting primary synchronization channel (P-SCH) that always available every 5 ms. Since the primary synchronization signal (PSS) is generated from a frequency-domain Zadoff-Chu sequence, it can be detected in frequency as well as time domain. Therefore, we are going to investigate them in term of complexity, latency, and recognition probability. This paper is organized as follows. In section II, the physical layer identities and P-SCH signals are briefly introduced. The frequency domain and time domain scheme are served in

The PSS sequences are generated from a frequency-domain Zadoff-Chu sequence [6] according to, ⎧ πun(n + 1) ⎪ ⎪ −j ⎪ ⎪ ⎨e 63 n = 0, 1, · · · , 30 du (n) = (1) πu(n + 1)(n + 2) ⎪ ⎪ −j ⎪ ⎪ ⎩e 63 n = 31, 32, · · · , 61

(2)

NID

root index u

0

25

1

29

2

34

where the Zadoff-Chu root sequence index (u) depend on (2) (NID ) as shown in Table.I Based on Eq.1, we can derive all possibilities of PSS as shown in Fig.1. Note that these figures are in frequency domain which horizontal axis expresses n and vertical axis belongs to du (n). As mentioned in [6], the sequence d(n) shall be mapped to the resource elements with frequency-domain index k and time-domain index l, according to ak,l = d(n),

n = 0, · · · , 61

(2)

DL RB Nsc NRB 2

(3)

k = n − 31 +

DL RB where NRB is downlink bandwidth configuration, and Nsc is resource block size in the frequency domain. Resource elements (k, l) for n = −5, −4, · · · , −1, 62, 63, · · · , 66 are reserved and not used for transmission of the PSS. Furthermore, we can derive time domain signal by mapping only PSS based on Eq.2 and Eq.3 in 2048 point IFFT as shown in Fig.2.

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

Real part

Imaginary part

( 2) ( 2) N ID = 0 N ID =1

Output

u = 25

Input signal

Cross-correlation

start

signal

FFT

Decision Logic

Delay

Frame Synchronization

Fig. 3.

PSS detection in frequency domain

to FFT input

u = 29

2048D

Conj.

input

144D

x

|.|2

+

+

+

Threshold

to FFT and decision logic

D

Fig. 4.

Cyclic prefix based frame synchronization

u = 34 Fig. 1.

PSS in frequency domain

Real

Imaginary

For u = 25

For u = 29

and 6 for frame structure type 2 [6], cyclic prefix length of the OFDM symbol that contain of PSS is 4.69 or 144 samples. Therefore, the beginning of OFDM symbol can be recognized using auto-correlation scheme as shown in Fig.4. Even though LTE introduce scalable bandwidth scheme with different FFT/IFFT point, the sub-carrier spacing is constant. Thus, we can only employ the 2048 point FFT instead of all possibilities. Cross-correlation function is introduced to recognize three physical layer identities. Cross-correlate of the receive signal r(n) with the stored pattern p(k) within N samples can be expressed as, Pcc (n) =

N −1 

p∗ (k) · r(n − k)

(4)

k=0

For u = 34

Fig. 2.

PSS in time domain

Yet, data field might occupy some resource elements either n < −5 or n > 63. As consequence, the output signals of IFFT are no longer similar to Fig.2. However, there are two ways to extract PSS, i.e. in frequency domain and time domain. Each scheme will be discussed separately below. III. F REQUENCY D OMAIN D ETECTION S CHEME The architecture of PSS detector in frequency domain is shown in Fig.3 that consists of frame synchronization, fast Fourier transform (FFT), cross-correlation and decision logic. Frame synchronization is generated by exploiting the cyclic prefix that preceding the OFDM frame. Since the PSS is mapped to the last OFDM symbol of slots 0 and 10 for frame structure type 1, and the third OFDM symbol of subframes 1

where p(k) is the pattern at k − th tap, and [∗ ] is conjugate function. The complexity of cross-correlation consists of N complex multipliers, N − 1 adders, N registers, and N patterns. Since one complex multiplier consists of four multipliers and three adders, totally 4N multipliers and 4N − 1 adders are involved in N tap cross-correlation computation. (2) (2) Since NID = 1 and NID = 2 are conjugate each other [7], (2) (2) we only need to recognize NID = 0 and NID = 1 instead of all possibilities as shown in Fig.5 in order to minimize the complexity. In further, 62 tap cross-correlation is introduced since PSS is only mapped into the 62 sub-carriers around zero sub-carrier. Cross-correlation output indicates the correlation index of the received signal with the stored pattern. Since there are three outputs, we should consider all possibilities by comparing each to others as shown in Fig.6 that referred as decision logic. IV. T IME D OMAIN D ETECTION S CHEME Fig.7 shows the architecture of PSS detector in time domain that consists of filtering, cross-correlation and decision logic. We employ low pass filter to extract PSS that mapped into the central band of entire bandwidth. To maintain the linearity

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

From cross-correlation

I-Input NID(2) = 0 Pattern

+

Multiplier

+

I Phase pattern

Multiplier

+

| . |2

-

control

NID(2) = 1

+

| . |2

-

NID(2) = 1 Pattern

NID(2) = 0

+

| . |2

|.

-

|2

NID(2) = 2

| . |2

+

Register R1

To decision logic

Selector

| . |2

Time index insert

Counter

= 5 ms

Peak power Comparison

no

yes

Multiplier

Register R2 Take Time index

Q-Input Multiplier

NID(2) = 0 Pattern (2)

NID = 1 Pattern

control

Q Phase pattern

control

−

Start signal from frame synchronization

Fig. 5.

Cross-correlation architecture

no

Register R3

≈0 yes

NID(2) = 0 Comparator NID(2) = 1

Logic

NID(2) = 2

Comparator

Output [0,1,2]

Logic

From crosscorrelation

Output [0,1,2] Comparator

Fig. 9. Fig. 6.

( 2) ( 2) N ID = 0 N ID =1

Input signal

Decision logic architectur in time-based detection

Decision logic architectur in frequency-based detection

Output

FIR filter

Down-sampling

Fig. 7. 30.72 MHz input

FIR1

Fig. 8.

Decision Logic

PSS detection in time domain

30.72 MHz

Ntap = 19 taps fs = 30.72 MHz fpass = 1.92 MHz fstop = 3.84 MHz

Cross-correlation

Down-sampling By factor 16

1.92 MHz Ntap fs fpass fstop

FIR2

1.92 MHz output

= 30 taps = 1.92 MHz = 0.465 MHz = 0.54 MHz

previous highest peak (stored in R2). Note that R1 and R2 store the peak power and time index that generated by a counter. Every a 5μs, the time index in highest peak (stored in R2) is compared with the highest peak on the previous time frame that stored in register R3. If the comparison result is satisfied, it means the PSS is available. V. D ETECTION S CHEMES C OMPARISONS Comparison is done on complexity, latency and recognition probability aspects. They will be served separately below.

Filtering configuration for time domain detection

A. Complexity Comparison in phase response, we propose finite impulse response (FIR) filter. Since PSS is mapped to 31 sub-carriers in the right and left side of DC sub-carrier with 15 kHz sub-carrier spacing, we take 465 kHz as the maximum frequency-pass. Furthermore, filter configuration as shown in Fig.8 is generated by FDAtool that available in Matlab blockset. Cross-correlation architecture has same architecture as frequency domain detection. However, PSS just occupy 62 subcarriers around DC sub-carrier regardless number of IFFT point. Thus, we can employ 128 tap cross-correlation after 16 times down-sampling in order to reduce complexity. There is no specific time frame to activate each block in time domain detection. It might generate error detection if the decision scheme in Fig.6 is employed. In order to minimize error detection, we introduce decision logic based on peak period as shown in Fig.9. The main concept is PSS should be detected every 5μs. Registers R1, and R2 are used to perform maximum peak detection. The highest peak is stored in registers R2. Every detected peak will be stored in register R1 and compared with

Physical layer identity detection is done in the mobile device side that has limited power and computation resources. Thus, low complexity and low power is the most consideration in the mobile device implementation aspect. Based on previous architecture that we have been discussed, complexity in term of computation resources can be estimated on Table II. Note that we just pay attention on the number of computation resources regardless the bit width. Even though time domain detection has higher complexity in filtering, cross-correlation, and decision logic processes, totally frequency domain detection has lower complexity than time domain detection due to FFT computation. However, the FFT complexity could be decreased by imploying low complexity architecture such as partial transforms [8] and using software defined radio (SDR) [9]. B. Latency Comparison Physical layer identity has to be recognized at cell search procedure before a connection is established. It should be done within a short latency. Therefore, processing latency is an important parameter in physical layer identity detection.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

TABLE II F REQUENCY AND TIME BASED COMPLEXITY COMPARISON Computation processes

TABLE III F REQUENCY AND TIME BASED LATENCY COMPARISON

Frequency-based

Time-based

-

2 × (19 + 30) 2 × (18 + 29) 2 × (18 + 29)

6 3 2 2192 1

-

2048 point FFT (1) Multiplication (2) Addition (3) Register

2(2048)(log2 2048) 2048(log2 2048) + (2048 − 1) 2048

-

Cross-correlation (1) Multiplication (2) Addition (3) Subtraction (4) Register (5) Memory

(4 × 62) + 6 (4 × 61) + 6 3 2 × 62 2 × 2 × 62

(4 × 128) + 6 (4 × 127) + 6 3 2 × 128 2 × 2 × 128

FIR filtering (1) Multiplication (2) Addition (3) Register Frame-synchronization (1) Multiplication (2) Addition (3) Subtraction (4) Register (5) Comparator

Decision logic (1) Subtraction (2) Register (3) Comparator (4) Counter Total (1) Multiplication (2) Addition (3) Subtraction (4) Register (5) Memory (6) Comparator (7) Counter

Computation processes

Frequency based

Time based

FIR filtering Frame synch. 2048 point FFT Cross-correlation Decision logic

2192 8219 2 0

49 2192 153600

Total

10413

155841

Reference number of tap one OFDM symbol see [11] one OFDM symbol 5ms timing frame

TABLE IV S IMULATION PARAMETER IN MODEL BASED RTL Number of iteration

100 LTE radio frames

(2) NID

0, 1, 2

IFFT point of TX

2048

Antenna scheme

SISO

Cyclic prefix type

Normal CP

Number of used subcarrier

1200

Frequency sampling

30.72 MHz AWGN

3 -

3×1 3×3 3×3 3×1

45316 24828 5 4364 248 4 -

616 608 6 350 512 9 3

Table III shows the processing latency comparison for frequency and time domain detection. In frequency domain, the longest processing latency occurs in FFT process while the longest process in time domain detection is in decision logic process. Using 30.72 MHz clock rate, we can estimate the total latency for both frequency and time domain detection are 0.34 ms and 5.07 ms respectively. However, it is not exceed of transition time to active state in LTE, i.e. less than 100 ms [4]. C. Recognition Probability Comparison In order to compare in term of recognition probability, we perform model based RTL [10] simulation using Symphony HLS blockset in MATLAB. Some parameters involved in this simulation are shown in Table IV. Note that, carrier frequency synchronization is ideal and channel compensation is not employed.

Fig. 10.

Channel model

extended pedestrian A (EPA)

SNR

-5dB to 15dB

Input bit width

10 bits

Multiplier bit width

16 bit

Adder bit width

vary from 16 to 24

LTE physical layer identity detection in AWGN channel model

Fig.10, and Fig.11, show LTE physical layer identities detection probability on AWGN and EPA channel models (2) for NID = 0. Considering AWGN channel, physical layer identities can be correctly recognized with probability at least 95% for SNR -5 dB or higher using time domain detection scheme. However, the frequency domain detection scheme shows the worse result than time domain scheme. The throughput of LTE shall be at least 95% of the maximum throughput with receiver sensitivity -91dBm on 20

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings

VI. C ONCLUSIONS

Fig. 11.

LTE physical layer identity detection in EPA channel model

We have discussed frequency domain and time domain based schemes for LTE physical layer identities detection. Frequency domain detection scheme consists of frame synchronization, FFT, cross-correlation and decision logic. However, time domain detection consists of filtering, cross-correlation and decision logic. Further, we have compared them in term of complexity, latency, and detection probability. From complexity point of view, frequency domain detection requires higher computation resources due to FFT involvement. However, low complexity of FFT architecture might be employed in order to increase the implementation possibility. The proposed time domain detection at least needs two PSS’s OFDM symbols to recognize physical layer identity. The consequence is long latency that equal to two PSS period (5 ms). However, frequency domain detection latency is mostly determined by FFT and frame synchronization processes latencies that based on our calculation around 0.34 ms. In this paper we also have done fixed point simulations in AWGN, and EPA channel models. As a result, the time domain detection scheme has better performance than frequency domain detection for both of channel models. However, employing high performance of frame synchronization on frequency domain scheme could give better detection result than time domain scheme. R EFERENCES

Fig. 12. LTE physical layer identity detection for the ideal frame synchronization in frequency domain detection scheme

MHz bandwidth [12]. Assuming 7 dB noise figure, antenna gain 0 dB, and minimum equivalent input noise for a receiver at 300K is -174 dBm/Hz, the minimum SNR requirement is −91 − (−174 + 10 log(20 × 106 ) + 7) = 3 dB on AWGN channel. Since the simulation result for time domain scheme shows that the PSS detection probabilities are 100% at SNR 3dB, time domain scheme satisfies the minimum requirement in term of receiver sensitivity. However, frequency domain scheme gives a worse result that does not meet the minimum requirement. The detection performances reduce for EPA channel models, as shown in Fig.11. However, time domain detection scheme still has superiority than frequency domain detection scheme. FFT process has high sensitivity to the beginning of the OFDM frame. Therefore, employing higher performance of frame synchronization detection could improve the LTE physical layer identities detection in frequency domain. To achieve the best result of frequency domain scheme regardless frame synchronization, we perform one more simulation in an ideal frame synchronization condition. As shown in Fig.12, the detection probability is far away from the previous one, and better than the time domain scheme results. Thus, in this case frequency domain detection gives better performance than time domain scheme.

[1] 3GPP Technical Specification Group Radio Access Network, Physical layer aspect for evolved Universal Terrestrial Radio Access (UTRA), 3GPP TS 25.814 v7.1.0, Sep. 2006. [2] Y. Tsai, G. Zhang, D. Grieco, F. Ozluturk, Cell Search in 3GPP Long Term Evolution Systems, IEEE Vehicular Technol. Magazine, vol.2, no.2, June 2007, pp.23-29. [3] Rohde Schwarz, UMTS Long Term Evolution (LTE)Technology Introduction, Application Note 1MA111, Sep. 2008. [4] Motorola, Long Term Evolution (LTE): A Technical Overview, Technical white paper, 2007 Available: http://www.motorola.com/staticfiles/ Business/Solutions/Industry%20Solutions/Service%20Providers/Wireless% 20Operators/LTE/ Document/Static%20Files/6834 MotDoc New.pdf. [5] S. Maruyama, S. Ogawa, K. Chiba, Mobile Terminals toward LTE and Requirements on Device Technologies, IEEE Symposium on VLSI circuit, pp. 2-5, Jun. 2007. [6] 3GPP Technical Specification Group Radio Access Network, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and Modulation, 3GPP TS 36.211 v8.7.0, May 2009. [7] H. Setiawan, and H. Ochi Time Domain Detection of Physical-Layer Identity for 3GPP Long Term Evolution, IEEE ICACT 2010, GangwonDo, Korea, pp. 8-13, Feb. 2010. [8] C.D. Murphy Low-Complexity FFT Structures for OFDM Transceivers, IEEE Trans. Commun., vol.50, no.12, pp.1878-1881, Dec. 2002. [9] G. Yang, and Y. Jung Design of Low-Complexity FFT Processor for MIMO-OFDM Based SDR Systems, IEEE ICACT 2010, GangwonDo, Korea, pp.107-110, Feb. 2010. [10] W.A. Syafei, Y. Nagao, K. Nishijo, A. Teramoto, M. Kurosaki and H. Ochi, Achieving Highly Reliable Wireless Communication System Design Using a Model-based RTL Design, IEEE GLOBECOM 2007, Washington DC, Nov. 2007. [11] Sheng-Yeng Peng, Kai-Ting Shr, Chao-Ming Chen, and Yuan-Hao Huang, Energy-Efficient 128∼2048/1536-point FFT Processor with Resource Block Mapping for 3GPP-LTE system, IEEE ICGCS’10, pp. 14-17, Shanghai, June 2010. [12] 3GPP Technical Specification Group Radio Access Network, Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception, 3GPP TS 36.101 v8.4.0, Dec. 2008.