Channel and Queue Aware Scheduling for Mixed Service in Multiuser

Channel and Queue Aware Scheduling for Mixed Service in Multiuser MIMO OFDM system Guangyi LIU1, Jianhua ZHANG, Jianchi Zhu, Weidong Wang, Research Institute of China Mobile1, Beijing University of Posts&Telecoms， Email：[email protected], [email protected] Abstract-In MIMO OFDMA system, multiuser diversity gain can be achieved both in spatial and frequency domain by channel aware scheduling. For Real Time (RT) service, the data rate and the packet delay should be guaranteed. But for None-Real Time (NRT) service, larger packet delay is acceptable and multiuser diversity gain can be exploited more to improve the system throughput. In this paper, by exploiting the queue status information and channel status information, a novel framework of unified scheduling for mixed RT and NRT services is proposed for MIMO OFDMA system, and two novel joint spatial-frequency scheduler based on Modified Proportional Fairness (MPF) and Quality Guaranteed (QG) priority function are proposed. From the simulation results, QG&MPF has much better performance than M2PF. For 2× 4 MIMO case, QG&MPF can support 40 RT user and 40 NRT users in 10MHz bandwidth simultaneously, and the system throughput of QG&MPF can approach 32Mbps; while M2PF can only support 15 RT users and 15 NRT users, and the system throughput is about 11.5Mbps. Key words: mixed service, MIMO, OFDM, QG, MPF

I. INTRODUCTION To fulfill the requirements of 3G LTE, MIMO and OFDMA are proposed for LTE downlink [1] for its excellent capability to mitigate the frequency selective fading of the mobile environment and provide high spectrum efficiency. Further, OFDMA provides a natural multiple access method by assigning different users with orthogonal subcarriers, and multiuser diversity gain in frequency domain can be exploited by subcarrier scheduling [2]. Besides, spatial multiuser diversity and multiplexing can be exploited to achieve better cell throughput. [4] [5] have proposed the improved round robin and greedy antenna scheduler respectively for multiuser downlink with VBLAST to exploit the spatial multiuser diversity. Naturally, by joint spatial-frequency scheduling [6] [7], the multiuser diversity gain can be achieved in spatial and frequency domain to improve the system throughput. In past, most work in this field is focused on maximizing the system throughput when the scheduler is channel aware [3] [4]. However, for different packet service, the burst characteristic of the packets varies much and influences the system throughput much. For RT service, usually the user data rate and packet delay should both be guaranteed. Obviously, if the packet status in queue is monitored and the packets can be transmitted in time, then better QoS can be guaranteed. In [8], the queue aware scheduling is considered in OFDMA, and better QoS can be guaranteed.

In this paper, based on multiuser Zero Force Beamforming (ZFB) [9], a unified framework based on spatial-frequency channel and queue aware is proposed for the scheduling of mixed RT and NRT services in MIMO OFDMA system, and two novel schedulers based QG and MPF are proposed. From the simulation results, QG&MPF has much better performance than M2PF. For 2× 4 MIMO case, QG&MPF can support 40 RT user and 40 NRT users in 10MHz bandwidth simultaneously, and the system throughput of QG&MPF can approach 32Mbps; while M2PF can only support 15 RT users and 15 NRT users, and the system throughput is about 11.5Mbps. II. SYSTEM MODEL OF MULTIUSER ZFB For MIMO OFDMA system, MIMO channel on every subcarrier can be regarded as flat fading MIMO, so the MIMO channel on subcarrier n can be expressed as Η i ,n , and the multiuser ZFB on subcarrier n can be described as Figure 1. For multiuser multiplexing, different antennas are selected from the UEs to receive the independent data streams from Node B without interference each other since ZFB is done at the transmitter. Assume the general multiuser MIMO channel matrix is expressed as ˆ = H  H H H ... n i, n K , n  , where H i , n is the MIMO  1, n ... channel matrix of user i with M T × M R dimension, and

M T , M R are the transmitter and receiver antenna number respectively. Then one antenna can be selected from every user as following. „ Antenna selection Algorithm (subopt) Step a: the MIMO channel matrix of M T users selected constructs a generalized MIMO channel matrix, and one receiver antenna is selected, which maximize the MISO channel capacity between it and the transmitter antennas; Then delete the left receiver antennas of the selected user from the generalized MIMO channel matrix. Step b: Select one receiver antenna from the left, and maximize the capacity of the MIMO channel between the selected receiver antenna and the transmitter. Step c: Repeat step b until every user obtains one receiver antenna. Assume that the antennas of K users are selected to receive, and then the virtual MIMO channel matrix between transmitter and the UEs selected can be constructed as: H n =  H1, n ... H i, n ... H K , n 

(1)

-------------------------------------------------------------------------------------------------------This work is funded by the 863 project of China under grant No.2006AA01Z258.

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Where H i is MIMO channel response of UE i on the selected receiver antennas. To guarantee the orthogonality among the independent data streams from Node B, the number of the selected receiver antenna should be fewer than that at the Node B. Then weight matrix for ZFB is [9]: B n = H †n ( H n H †n ) Dn −1

(

(2)

)

Where Dn = diag d1,n ,...d k , n ,..., d M R , n is the diagonal matrix which keeps the transmit power unchanged after beamforming, and † means the hermit transpose. M R is the antenna number selected at the virtual UE, it is also the independent data stream number. dk,n =

1

( H H † ) −1   n n  k , k

Prnk =

(3)

Rˆk ,n ( t ) × f ( PLRk ,Wk ( t ) ) Rˆ

(8)

k

Where,

If M R receiver antennas are selected, and S ∈ ^ M ×1 is the modulated symbol vector, the element sk is the data symbol on the stream k , the transmitted signal after beamforming is: xn = Bn S n (4) The total signal of all the UE after the channel is: rn = H n xn + n = H n B n S n + n = Dn S n + n

Where Buff _ Len is the buffer length of user k ; rS is the service data rate, Pdrop is the number of the dropped packets which exceeds the maximum packet delay, and Psent is the number of the packets transmitted. „ QG For real time service, the service has strict constraint on delay, but permits some packet drop ratio. To maximize the user number serving by the system, system may permit every user has a small packet drop ratio less than the pre-defined one. So we proposed a modified priority function, Quality Guaranteed (QG) for real time service as:

 10Wi ( t ) / WMax ×10 PLRi / PLRMax ,  if PLRMax ≥ PLRi  f ( PLRi , Wi ( t )) =  W (t ) / W ( 2 PLRMax − PLRi ) / PLRMax , 10 i Max × 10  if PLR < PLR Max i 

(5)

(9)

Because Dn is a diagonal matrix, the MIMO channel is decomposed into M R SISO channels with channel gain d k , n respectively on subcarrier n .

Where Wi ( t ) is the maximum packet delay in queue,

III. JOINT SPATIAL-FREQUENCY SCHEDULING BASED ON CHANNEL AND QUEUE AWARE

Firstly, three priority functions for RT and NRT are proposed, then based on these priority functions, the joint spatial and frequency scheduling algorithms for mixed RT and NRT service are proposed. A. Priority function definition „ MPF for NRT For NRT service, the MPF priority function is defined as:

Prnk (t ) =

 rmin − rk   Rnk (t )  × exp    Rk (t )   rmin 

(6)

Where Rnk (t ) is the averaged transmission capability of user k on subcarrier n , rk is the averaged user data rate since the service setup, rmin is the required minimum data rate, Rk (t ) is the average data rate of user k . For RT service, the MPF and QG priority function are defined as follow. „ MPF for RT

Prnk (t ) =

Rnk (t ) Buff _ Len Pdrop + Psent × × Rk (t ) rS Psent

Wmax

is the permitted maximum packet delay for every

service, PLRi ( t ) is the current packet loss ratio, PLRmax is the permitted maximum packet loss ratio. When PLRi is small, the priority value of QG is less than that

of

Proportional

Fairness;

when

PLRi

approaches PLRMax , the priority increases fast; when PLRi exceeds PLRMax , the priority is decreased to avoid wasting on the radio resource by the user with bad channel condition. B. Unified Scheduling Frameworks for mixed services In multi-cell scenario, since the inter-cell interference should be taken into account in the subcarrier and power allocation, multi-cell coordination is necessary for the centralized algorithm, which will lead to complex system architecture and heavy signaling overhead. In this paper, the distributed control is considered, and no multi-cell coordination is necessary. The power of Node B is distributed uniformly on every spatial sub-channel of every subcarrier, and thus the scheduling problem is simplified as multiuser subcarrier allocation and multiuser antenna selection. The joint spatial-frequency channel and queue aware scheduling algorithm can be proposed as following: Step 1: On every subcarrier, the priority of RT and NRT service is calculated according to the corresponding priority function defined above.

(7)

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Step 2: On every subcarrier, M T users with highest

IV.

SIMULATION PARAMETER

priority value are chosen to construct the user set U n and ˆ = H general MIMO matrix H n  1, n ... H i , n ... H K , n  . Step 3: By antenna selection algorithm proposed in section II and the spatial sub-channel gain d k , n for Mu-ZFB ˆ , the proper MCS for every user is is calculated based on H

In the simulation, MCS adopted is given in Table 1, the convolution coding are combined with QPSK, 16QAM to create 5 MCS. No H-ARQ is considered in link level simulation, but the chase combining is adopted in system level simulation. The suitable MCS is selected from the Table 1 to transmit data symbol on every subcarrier. The SNR threshold for MCS as Table 1 is obtained.

decided according to the SINR. The Subopt algorithm can be used to construct the spatial sub-channel for every subcarrier.

TABLE 1 THE MCS AND THE SNR THRESHOLD

n

Step 4: Every user’s averaged data rate Rˆi is updated as: (1 − α ) Rˆi + α Ri , if user k is served . Rˆi =  (10) else  (1 − α ) Rˆi , Where 0 < α < 1 is the forgetting factor. N

Ri = ∑ ri , n is the served data rate in current scheduling

Code Ratio

data bits

QPSK

1/3

2/3

0.5dB

QPSK

1/2

1

3.7dB

3

QPSK

3/4

3/2

6.3dB

4

16QAM

1/2

2

10dB

5

16QAM

3/4

3

15.2dB

MCS

Mod

1 2

SNR threshold

n =1

period, ri, n is the data rate on subcarrier n of user i . If subcarrier n is not allocated to user i , ri,n = 0 .

C. Scheduling algorithms for mixed services Based on the unified scheduling framework, two scheduling algorithms for mixed RT and NRT services are proposed for MIMO OFDMA system. For M2PF algorithm, the priority of both RT and NRT service is calculated by the MPF priority function definition. For QG&MPF algorithm, the priority of RT service is calculated by QG priority function, and that of NRT service is calculated by MPF function. „ M2PF According to the averaged transmission capability Rnk ( t ) of user k on subcarrier n , the required minimum i

data rate Rmin , the averaged user throughput ri , the buffer length Buff _ Len , and the service data rate rS , calculate the RT and NRT user’s priority on subcarrier n by MPF priority function for RT and NRT service respectively. The other processes are the same as that of the unified scheduling framework described above. „ QG &MPF According to the averaged transmission capability k Rn ( t ) of user k on subcarrier n , the required minimum i

data rate Rmin , the averaged user throughput rk , the current packet loss ratio PLRi ( t ) , permitted maximum packet loss ratio PLRmax , the maximum packet delay

Wi ( t )

in queue, the

permitted maximum packet delay for every service

Wmax

,

service data rate rS , calculate the RT and NRT user’s priority on subcarrier n based on the QG and MPF. The other processes are the same as that of the unified scheduling framework described above.

To avoid heavy signaling loading, every continuous 8 subcarriers are combined together as a basic resource unit, named as a sub-channel, in which the same modulation and coding scheme is used for all subcarrier. One sub-channel is assigned to different user according to the algorithms we proposed in section III. The power of the Node B is uniformly distributed on all spatial sub-channel and subcarriers. The same frame structure as LCR TDD for LTE TDD [10] is adopted in our work. The soft frequency reuse scheme is adopted and optimized as [6]. The 500kbit/s video traffic model is defined as Table 2. For NRT service, the full buffer data is assumed. TABLE 2 TRAFFIC MODEL PARAMETERS Video Stream model Inter-arrival time between the beginning of each video-frame Number of video-packet in a frame Video packet size Inter-arrival between video-packets in a video frame Video packet max delay time Video average data rate Video minimum data rate Video length

Value 100ms 8 400byte Truncated Pareto K=2.5 alpha = 1.2 M= 12.5ms 200ms 500kbit/s 128kbit/s 120s

If the user has 5% packets are dropped during the communication, then the video user is regarded as unsatisfied. The other system simulation parameters are the same as that in [6].

V. SIMULATION RESULTS From the Figure 1, Figure 2 and Figure 3, the performance of QG&MPF is obviously better than M2PF. As the user number increase, multiple users compete the same resource, the difference between schedulers become obvious. The more the users, the larger the performance

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difference between QG&MPF and M2PF. For 2× 4 MIMO, when the user number of the RT and NRT are both 30, the system throughput difference between M2PF and QG&MPF exceeds 100％. Since M2PF usually allocate the resource for RT service in higher priority, and the throughput improvement provided by multiuser diversity gain of NRT users are sacrificed to guarantee the data rate of RT services. So QG&MPF has much better performance than M2PF.

of RT services. So QG&MPF achieves much better performance than M2PF. For 2× 4 MIMO case, QG&MPF can support 40 RT user and 40 NRT users in 10MHz bandwidth simultaneously, and the system throughput of QG&MPF can approach 32Mbps; while M2PF can only support 15 RT users and 15 NRT users, and the system throughput is about 11.5Mbps. ZFB MIMO OFDM, Mixed service 35

ZFB MIMO OFDM, UE Drop Ratio of Mixed Service

30

0.25

system throughput (Mbps)

QG&MPF 2x4 NRT QG&MPF 1x2 NRT QG&MPF 1x2 RT QG&MPF 2x4 RT M2PF 2x4 NRT M2PF 2x4 RT

UE Drop Ratio

0.2

0.15

20

15

0.1

10

0.05

5

0

5

10

15

20 25 UE number per cell

30

35

ZFB MIMO OFDM, Mixed service

[2]

QG&MPF 2x4 NRT QG&MPF 1x2 NRT QG&MPF 1x2 RT QG&MPF 2x4 RT M2PF 2x4 NRT M2PF 2x4 NRT

1600

[3]

[4]

1400 1200

[5]

1000 800

[6]

600 400

[7] 5

10

15

20 25 UE number per cell

5

10

15

20 25 UE number per cell

30

35

40

REFERENCES [1]

2000 1800

QG&MPF 2x4 QG&MPF 1x2 M2PF 2x4

Figure 3 Mixed services: system throughput vs. user number

40

Figure 1 Mixed services: user number vs. drop ratio

UE Data Rate (kbps)

25

30

35

40

Figure 2 Mixed services: average user data rate vs. user number

3GPP TSG RAN WG1 #42, R1-050789, Text Proposal on “TDD UL/DL based on OFDMA” for TR 25.814. Cheong Yui Wong, Cheng, R. S, et al., ”Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation,” IEEE Journal on Selected Areas in communication, vol. 17, pp. 1747 -1758, Oct. 1999. G. Foschini, M. Gans, “On limits of wireless communications in a fading environment when using multiple antennas”, IEEE Wireless Personal Communications, March 1998, 6(3): 311-335. Oh-soon Shin, Kwang Bok(Ed) Lee, “Antenna-Assisted Round Robin Scheduling for MIMO Cellular Systems”, IEEE Communications Letters, vol. 7, no. 3, pp. 109-111, Mar. 2005. Manish airy, Sanjay Shakkottai and Robert W. Health, “Spatial Greedy scheduling in multiuser MIMO wireless system”, Proc. of IEEE Asilomar Conf. on Signals, Systems, and Computers, vol. 1, pp. 982 - 986, Pacific Grove, CA, USA, Nov. 9-12, 2003. Guangyi Liu , et al, “Enhanced perforamnce of TD-SCDMA LTE system With Multiuser MIMO in downlink”, IEEE APCC 2006. Guangyi Liu, Jianhua Zhang, et al, “Joint Space-Frequency opportunistic Multiuser Diversity for VBLAST OFDMA with Partial CSI”, IEEE ICCCAS 2006.

[8] Vincent K. N. La, et al, “Channel Adaptive Technologies and Cross Layer Designs for Wireless Systems with Multiple Antennas Theory and Applications”, Feb.2006, Wiley. [9]

VI. CONCLUSION In this paper, a unified framework of a joint spatialfrequency scheduling based on channel and queue aware for mixed service in MIMO OFDMA system is proposed, and a joint spatial-frequency scheduling algorithm is proposed as QG&MPF and M2PF. Since M2PF usually allocate the resource for RT service in higher priority, and the throughput improvement provided by multiuser diversity gain of NRT users are sacrificed to guarantee the data rate

Jinsu Kim1, Sungwoo Park1, et al, “A Scheduling Algorithm Combined with Zero-forcing Beamforming for a Multiuser MIMO Wireless System”, IEEE VTC 2005Fall. [10] 3GPP TSG RAN WG1#42, R1-050800, “Numerology and Frame Structure of EUTRA TDD based on OFDMA and text proposal for TR 25.814 “, CATT, RITT, ZTE, Huawei.

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