Adaptive Radio Resource Allocation with Novel Priority Strategy Considering Resource Fairness in OFDM-relay System Wang Ying1,2, Wu Tong1,2, Huang Jing1,2, Shu Chao1,2, Yu Xinmin1,2, Zhang Ping1,2 1: Key Laboratory of Universal Wireless Communication, Ministry of Education 2: Wireless Technology Innovation Institute, Beijing University of Posts and Telecommunications P.O. Box 92, BUPT, 100876 Beijing, China
[email protected] Abstract—Relaying transmission is a candidate way to combat wireless channel fading and enlarge the coverage, and efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, an adaptive multiuser radio resource allocation model is proposed for the downlink of OFDM-relay system, which exploits performance gain in both frequency domain and time domain. According to the different transmission modes, two QoS-oriented scheduling algorithms based on the feedback of the channel state information (CSI) of two hops are investigated. One is enhanced proportional fairness (EPF) algorithm, and the other is improved priority (IPRI) algorithm. Both of them can achieve high system throughput and better resource fairness due to the adaptive allocation, especially in the QoS-guarantee aspect for cell edgy users compared with conventional scheduling schemes. The priority strategy is a novel scheme, because of considering resource fairness with artificial starve (AS) state in IPRI, which yields higher spectral efficiency and achieve better data rate requirements for the users. Keywords- OFDM-relay; adaptive resource allocation; QoSoriented; EPF; IPRI; AS
I.
INTRODUCTION
B3G system is requested to provide the high rate service in high speed, enhance the system throughput and enlarge the coverage of service [1]. Orthogonal Frequency Division Multiplex (OFDM) is a candidate technology for the future mobile communication networks [2], and it is known to be robust against frequency selective channels (FSC) since it transforms the FSC into several parallel flat-fading channels [3]. The implementation of OFDM is very simple using IFFT/FFT, and its inherent multicarrier nature provides a high degree of flexibility for adaptive modulation coding (AMC). Alternatively, relayed transmission is an effective way to combat wireless channel fading and to exploit distributed spatial diversity [4]. Combined with these two technologies, OFDM-relay system can make full use of frequency diversity, user diversity and spatial diversity in multiuser scenario [5-7]. Therefore, OFDM-relay will be good promising technology for future wireless communication. Some current researches show that the performance of
relaying networks can be further improved by optimally distributing the overall power between two hops according to water-filling method [8-9]. In [8], optimal power allocation method is presented to minimize the outage probability of Rayleigh fading channel. The optimal power allocation to maximize the system capacity is proposed for regenerative relaying scheme in [9]. Besides, some work on subcarrier allocation has being carried in [10-14], most of which focus on exploring multiuser diversity. Ref.[10] aimes at minimizing the total transmitted power and satisfying a minimum rate constraint for each user. In [11], a low-complexity suboptimal algorithm is proposed, and the problem is divided into two sub-problems. One is to find the required power and the number of subcarriers for each user, and the other to find the exact subcarrier and rate allocation respectively. In [12], the problem is formulated using a min-max criterion for downlink, and a low-complexity suboptimal algorithm is developed. Real-time subcarrier allocation schemes are studied in [13] and [14], and suboptimal algorithm is developed to simplify the Hungarian algorithm and achieve similar performances. Therefore, it is necessary to investigate adaptive scheduling algorithms for the future relaying network. This paper focuses on investigating the adaptive radio resource allocation in multiuser OFDM-relay system, which can improve the system performance further. The major contributions of this paper are as follows: 1) a novel model based on OFDM-relay is proposed to exploit the wireless channel characteristic in both frequency domain and time domain for different transmission mode. 2) the adaptive scheduling algorithms can achieve better performance and guarantee the QoS for cell edgy users based on the above model, where the complexity of these algorithms are not increased too much compared with the traditional PF algorithm. 3) the IPRI scheduling strategy with artificial starve (AS) state to improve the resource fairness in PRI matrix of all users is a novel scheme for B3G communication. II.
SYSTEM CONFIGURATION AND ALLOCATION MODEL
A. System Configuration Description
This paper is financed by Ericsson Company and is also supported by National Natural Science Foundation of China (60496312)
1-4244-0264-6/07/$25.00 ©2007 IEEE
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A relay enhanced cellular network based on OFDM-TDMA is proposed with adaptive allocation scheme. The node B is located at the center of the cell and six fixed relay nodes (RNs) are placed uniformly around the node B. Each RN is located at 2/3 radius away from the node B so as to achieve the optimal system performance according to [15].
2 § Pb (n, j ) × h0k (n, j ) · B k ¨ ¸ (3) C (n, j ) = log 2 1 + α ¨ ¸ N n0 × ( B / N ) © ¹ where h0k (n, j ) is the instantaneous channel state information (ICSI) of the (nth , j th ) chunk for k th direct user. k d
According to the OFDM/TDMA transmission mode, the chunk is divided into 2 sub-chunks for relaying user. Therefore, the weight factor is set by 1/ 2 for relaying users. Define that Crm (1) (n, j ) , Crm (2) (n, j ) is the capacity of the first hop and the second hop respectively. If the (nth , j th ) chunk is allocated to the mth relaying user adopting regenerative relaying mode, the capacity can be expressed as: C
m (1) r
Fig. 1 Adaptive resource allocation in OFDM-relay system
As shown in Fig 1, the total radio resource is partitioned in both frequency and time domain. In particular, the frequency resource is divided into N frequency blocks, each of which contains a cluster of subcarriers. The total time resource T is divided into J TTI. The nth frequency block and the j th TTI construct the (nth , j th ) freq-time chunk. The freq-time chunk will be divided into two sub-chunks across time domain for the relaying users. The adaptive resource allocation algorithms aim to allocate the available chunks for both direct users and relaying users, which can guarantee the QoS of different users and enhance the system performance. B. Allocation and System Model in OFDM-relay Networks Consider the downlink of an OFDM-relay system with M relaying users and K direct users served by APs. The total frequency band is B and the bandwidth of each freq-time chunk is B / N . Assume that Thr is the total throughput of the system, and C rm is the capacity of the mth ( m = 1," M ) relaying user, and Cdk is the capacity of the k th ( k = 1," K ) direct user. The overall system throughput can be defined as: M
m r
K
Thr = ¦ C + ¦ C m =1
k =1
k d
(1)
Assume that the transmission power of the node B in each chunk is Pb (n, j ) , and the transmission power of each RN in each sub-band is Pr (n, j ) . The variance of the Additive White Gaussian Noise (AWGN) is σ 2 = n0 ∆f = n0 ( B / N ) , where n0 is the power spectral density of the noise. The required k , where k is the total minimum data rate for each user is Rmin user set. The Bit Error Rate (BER) requirement is BER k , and the α k is the SNR gap for each user, where:
α k = 1.5 /(− ln(5 BER k )) th
th
(2)
For the direct users, if the (n , j ) chunk is allocated to the k th direct user, the capacity of the k th direct user can be expressed as:
2 § P (n, j ) × h1m (n, j ) B m b ¨ ( n, j ) = log 2 1 + α ¨ 2N n0 × ( B / N ) ©
· ¸ ¸ ¹
(4)
2 § Pr (n, j ) × h2m (n, j ) · B ¸ log 2 ¨1 + α m ¨ ¸ 2N n0 × ( B / N ) © ¹ Crm (n, j ) = min[Crm (1) (n, j ), Crm (2) (n, j )]
Crm (2) (n, j ) =
m 1 th
(5) (6) th
th
where h (n, j ) is ICSI of the first hop of the (n , j ) chunk for m relaying user, and h2m (n, j ) is the ICSI of the second hop, respectively. Define ξ n, j ,k to be the assignment indicator of the resource chunk for the k th user making use of (nth , j th ) chunk. That is , ξ n , j ,k = 1 if and only if (nth , j th ) chunk is assigned to k th user. Otherwise, it meets ξ n, j ,k ′ = 0 ∀k ′ ≠ k . Therefore, the available data rate for direct user and relaying user is: Cdk =
1 N J k ¦¦ Cd (n, j) ⋅ ξn, j , k T n =1 j =1
(7)
1 N J m (8) ¦¦ Cr (n, j ) ⋅ ξn , j ,m T n =1 j =1 Put (7), (8) into (1), the total throughput of the OFDMrelay system will be described as: Crm =
Thr =
M N J · 1§ K N J k m ¨ ¦¦¦ Cd (n, j )ξ n , j ,k + ¦¦¦ Cr (n, j )ξ n , j ,m ¸ (9) T © k =1 n =1 j =1 m =1 n =1 j =1 ¹
inner Define ρouter is the proportion of the average data rate of inner zone users to outer zone users (cell edgy users), which reveals the resource fairness of OFDM-relay system. Assume C outer and C inner is the average data rate of outer zone and inner zone respectively. Then,
C inner (10) C outer The aim of the adaptive resource allocation scheme is not only to maximize the total throughput while satifying the user’s data rate and BER requirements, but also to guarantee the resource fairness of the different users to make a optimal tradeoff. Therefore, based on the above analysis, the scheduling problems can be written as: inner ρouter =
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M N J § K N J k · m °°max ¨ ¦¦¦ Cd (n, j )ξ n , j , k + ¦¦¦ Cr (n, j )ξ n, j , m ¸ m =1 n =1 j =1 © k =1 n =1 j =1 ¹ (11) ® ° inner k m °¯min( ρouter ), ∀Cd , Cr , ξ n, j , k , C outer , C inner
Subject to: k k Cdk ≥ Rmin , Crk ≥ Rmin (12) If ξ n , j ,k = 1 , otherwise ξ n, j ,k ′ = 0 ∀k ′ ≠ k (13) However, this is a Nonlinear Programming problem with N ⋅ J ⋅ ( K + M ) variables. It is difficult to get the optimal solution. The sub-optimal scheduling algorithms with novel adaptive schemes are investigated in next part, which are the available strategies dealing with the NP problem.
III.
ADAPTIVE RADIO RESOURCE ALLOCATION SCHEMES
The adaptive radio resource allocation algorithms should be able to exploit the time-varying and frequency-selectivity channel conditions of users to achieve maximization of throughput and higher utilization of wireless resources, while guaranteeing the fairness among users, especially in relaying system to guarantee the requirements for both direct users and relaying users. A. Enhanced Proportional Fairness(EPF) Scheduling The EPF scheduling algorithm takes advantage of the timevarying fading characteristics of wireless channels. Therefore, it achieves throughput gain during some window of past transmission and multiuser diversity gain in time domain. The proportional fair factor fk* (n, j ) is described as: f k ( n, j ) = µ k ⋅ *
k Ccurrent ( n, j )
R k (t )
k * = arg max { f k * (n, j )}
(15)
§ 1· 1 i (t ) Rk (t + 1) = ¨1 − ¸ Rk (t ) + Ccurrent (16) T T c c © ¹ where Tc denotes the time window constant. The parameter µk is the adaptive weight factor for the ratio of the inner . It is decribed as: data rates of two hop to minimize ρouter ¯C
m (1) r
1 / Crm (2)
if k ∈ {direct users} if k ∈ {relaying users}
(17)
If Crm (1) / Crm (2) ≥ 1 ,that is µk ≥ 1 , which means the relaying user is the cell edgy zone, and the priority of the cell edgy user will be enhanced considering the resource fairness through the effect of µk . If µk < 1 , and the reverse scenario appears. The allocation for each user will meet: ∆ rate =
1 N J k k ≥0 ¦¦ Ccurrent (n, j ) ⋅ ξ n, j ,k − Rmin T n =1 j =1
B. A Novel QoS-oriented Improved PRI (IPRI) Scheduling The QoS-oriented scheduling algorithm is a tradeoff between the system and the users. The IPRI allocation strategy takes into both resource fairness and QoS reqiurement. In order to achieve the objectives of (11), the priority of IPRI for each user is described as: PRIk (n, j ) = α ⋅ PRI k(1) ( n, j ) + β ⋅ PRI k( 2) ( n, j ) + γ ⋅ PRI k(3) ( n, j ) (19)
where PRI k (n, j ) denotes the improved PRI in OFDMrelay network, while PRIk (i ) (n, j ) , i = 1, 2,3 is three aspects considered. With the CSI feedback, the data rate matrix CKchunk (n, j ) for each user in (nth , j th ) chunk will be calculated. ª C1,1, j « C CKchunk (n, j ) = « 2,1, j « # « C ¬« ( M + K ),1, j
(18)
C1,2, j C2,2, j # C( M + K ),2, j
C1, N , j
º » " C2, N , j » (20) » % # » " C( M + K ), N , j ¼» "
where k Ccurrent (n, j ) = Ck , n, j
(21)
Define PRIk(1) (n, j ) , PRI k(2) (n, j ) as follows: PRIk(1) (n, j ) =
(14)
k where Ccurrent (n, j ) is the current data rate for the k th user th th using (n , j ) chunk using (7) and (8), respectively. R k (t ) denotes the average data rate of the k th user received up to time t :
µk = ®
The EPF allocation algorithm will achieve throughput by (15) and resource fairness by (17).
k Ccurrent ( n, j )
K (n, j )} max {Cchunk
PRIk(2) (n, j ) = dk (n, j ) / Rcell
(22) (23)
where dk (n, j ) denotes the distance from the k th user to the node B. Rcell is the radius of each cell. The throughput and resource fairness could be well handled through the effect of PRIk(1) (n, j ) and PRI k(2) (n, j ) . α and β are the non-negative weight factor of them, with constrains as:
α + β =1
(24)
PRI k(3) (n, j ) is the novel consideration in this algorithm. It denotes the artificial starve (AS) state for the k th user, which means the k th user can get some data rate, but can not meet the requirement for a preriod time. The artificial starve function f (a.s.) is introduced, which meets (0,1) distribution: PRIk(3) (n, j ) = f (a.s.) If the k th user meets:
(
(25)
)
k Ccurrent n,τ k | τ k ∈ ( j1 , j2 ... jTξ ) < max {CKchunk (n,τ k )} ⋅η (26) k where Ccurrent (n,τ k ) denotes the average data rate for a period of time lasts Tξ , max {CKchunk (n,τ k )} is the maximum data rate in each TTI of CKchunk (n, j ) . And η is the percentage weight factor. If (26) is met, the function f (a.s.) will be set to 1, then the priority of k th user will be enhanced by γ according to
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(19), which is the priority gap. Otherwise, the function meets f (a.s.) = 0 , that is PRIk(3) (n, j ) = 0 . Therefore, the PRI matrix PRI Kchunk (n, j ) can be achieved. ª PRI1,1, j « PRI 2,1, j K PRI chunk ( n, j ) = « « # « PRI ( M + K ),1, j ¬«
PRI1,2, j
"
PRI 2,2, j #
" %
PRI( M + K ),2, j
PRI1, N , j º » PRI 2, N , j » » # » " PRI ( M + K ), N , j ¼» (27)
where PRI k ( n, j ) = PRI k ,n , j
(28)
The adaptive resource allocation is implemented according to (27). The IPRI algorithm will also meet (18) to guarantee the QoS of different users. And the value of α , β and γ can be adjusted in simulation in order to achieve the better system performance, which can enhance the system throughput to maximize Thr and ensure the resource fairness to minimize inner ρouter , simultaneously.
observed by the following regulations: • Compared to relay systems, the RR scheduling in conventional network (RR w/o relay) performs worse, which reveals the advantage of the relaying network. • In OFDM-relay network, IPRI algorithm brings the most throughputs while RR provides the least among these scheduling schemes. IPRI with coefficients pair ( α = 0.6 , β = 0.4 ) provides fewer throughputs than IPRI with coefficients pair ( α = 0.7 , β = 0.3 ) does, because it is more sensitive to the distance between the users and BS, and gives more consideration to those users who are far from the BS. • Similarly, EPF brings less throughput gain than IPRI. With the user number increases above 45, the throughput brought by EPF decreases slightly because more resources are allocated to users with inferior second hop link and thus produces an influence on the total throughput. 35
ITEM
RR
ERF
IPRI
Complexity Throughput Spectral Efficiency inner Fairness( ρouter )
Normal Low Low Normal
High High High Superior to RR
Higher Higher Higher Better
IV. SIMULATION RESULTS AND DISCUSSION In the simulations, a number of random fading coefficients are generated for each position in the simulated area. Here the fading coefficients are assumed to be Rayleigh distribution. The simulation parameters are listed in TABLE II. TABLE II.
25
20
15
10
ALGORITHM S COMPARISONS
LIST OF SIMULATION PARAMETERS
Parameters
Values
Parameters
Values
Cell Radius RN Radius RN number
1000m (2/3)*1000 6 per cell
BS Power RN Power Subcarrier num
Standard Deviation of Shadowing
8dB
Path-Loss Factor
Noise Power Carrier Freqency Subcarrier Bandwidth BS power (w/o relay)
-143dBw 2GHz 20kHz 10.5w
Target BER Chunk num TTI Doppler Frequency
10w 0.5w 1024 BS/MS: 4; BS/RN: 3.5; RN/MS: 4; 10-5 128 2ms 5.6Hz
According to the system model, system throughput, data rate ratio and spectral effiencicy are the significant parameters investigated for the different scheduling algorithms in OFDMrelay system . Fig 2 shows the throughput with different scheduling algorithms in the relaying system. It can be
5 30
35
40
45
50
55
User Number per Cell
Fig. 2 System throughput of different scheduling algorithms 5
4.5
Capacity Ratio ρ
TABLE I.
Throughput [Mbps]
30
C. The Complexity Comparision of the Schemes Compare with the conventional scheduling algorithm, e.g. Round Robin (RR) scheduling algorithm, the complexity of EPF and IPRI increases. However, within the complexity tolerance, both of them aim to achieve better system performance and resource fairness sacrificing the system costs. The comparisons are listed in TABLE I.
RR w/o Relay RR EPF IPRI(α =0.7,β =0.3) IPRI(α =0.6,β =0.4)
4
3.5
RR w/o Relay RR EPF IPRI(α =0.7,β =0.3) IPRI(α =0.6,β =0.4)
3
2.5
2 30
35
40
45
50
55
User Number per Cell
Fig. 3 System throughput of different scheduling algorithms
In Fig 3, axis-y denotes average user capacity ratio between inner users and outer users apart from BS at least 80% of the cell radius. Seen from the figure, EPF and IPRI allocate more available resources to edge users adaptively, and thus narrow the gap between inner and outer users when the chunks of the system are insufficient, which can achieve the aim for inner minimum ρouter better. Capacity ratio of RR is not affected by user number, which is always a horizontal line through the
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variation of the user number. Obviously, the data rate of the cell edgy users can be guaranteed and the better resource fairness can be achieved for the proposed schemes, while the conventional RR algorithm can not do. 1 0.9 0.8
Probability
0.7 0.6 0.5
Proportional Fairness (EPF) algorithm, and the other is the Improved PRI (IPRI) algorithm. EPF gives higher priority to edge users to utilize the channel resources and IPRI involves factors of the user capacity, location and artificial starve (AS) status. These schemes take into account both the system throughput and the resource fairness. Simulation results indicate that IPRI improves the spectral efficiency efficiently and meanwhile guarantee the QoS of edge users. EPF, as a good choice for relay systems, enhances the performance of users whose second hop link quality are inferior and is a good tradeoff between system performances and algorithm complexity.
0.4 0.3 0.2 0.1 0
ACKNOWLEDGMENT
RR w/o Relay RR EPF IPRI(α =0.7,β =0.3) IPRI(α =0.6,β =0.4) 0
0.5
1
1.5
2
2.5
3
3.5
The authors of this paper thank Miao Qingyu, Zhang Zhang, and Wang Hai for their helpful discussions and suggestions. Their support is gratefully acknowledged. 4
REFERENCES
Spectral Efficiency [bit/s/Hz]
Throughput [Kbps]
Fig. 4 The spectral efficiency CDF of different scheduling algorithms 2000 RR w/o Relay RR EPF
1500
[2]
1000
[3]
500 0
1
2
3
4
5
6
7
8
[4]
User Index Throughput [Kbps]
[1]
2000 IPRI(α =0.7,β =0.3)
1500
IPRI(α =0.6,β =0.4) EPF
1000 500 0
[5]
[6] 1
2
3
4
5
6
7
8
User Index
Fig. 5 The average throughput of sampled users of different scheduling algorithms in OFDM-relay network
The capacity CDF curves of distinct scheduling algorithms are depicted in Fig 4. It can be observed that the EPF scheme improves the user fairness since the CDF curves of these sorts of users are close to the RR curves. IPRI obtains the most spectrum efficiency gain than other schemes due to its throughput and distance factors which takes both the capacities and locations of users into account. The average throughput of 8 sampled users are shown in Fig 5, where the first five users are inner ones and the last three users are edgy users. It is clear that the proposed EPF and IPRI scheduling algorithms can not only ahieve the system performance gain, but also can guarantee the data rate for each user to ensure the resource fairness especially for the edgy users. Generally, compared with EPF and IPRI, IPRI is the relatively optimal scheduling while EPF is also a good choice for the tradeoff of the system performance and the algorithm complexity. V.
CONCLUSION
This paper investigates two QoS-oriented scheduling algorithms in OFDM-relay network. One is the Enhanced
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