Dynamic Energy Saving Subcarrier, Bit and Power Allocation in OFDMA Relay Networks

DIGITAL COMMUNICATIONS Dynamic Energy Saving Subcarrier Bit and Power Allocation in OFDMA Relay Networs HUANG Bo FANG Xuming* ZHAO Yue CHEN Yu HE Rong Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu 6131 China Abstract: To reduce energy consumption while maintaining users Quality of Service (QoS) in Orthogonal Frequency Division Multiplex Access (OFDMA) relay-enhanced networs an adaptive energy saving subcarrier bit and power allocation scheme is presented. The optimal subcarrier bit and power allocation problems based on discrete adaptive modulation and coding scheme have been previously formulated for relay-enhanced networs and have been reformulated into and solved by integer programming in optimization theory. If the system still has a surplus of subcarriers after resource allocation we carry out Bandwidth Exchange (BE) to enable more subcarriers to participate in transmission to save energy. In addition as the relay selection scheme is closely lined with resource allocation a heuristic energy saving relay selection scheme is proposed. Simulation results indicate that the proposed algorithm consumes less energy when transmitting the same number of bits than greedy energy saving schemes although its spectrum efficiency is worse. Key words: OFDMA; resource allocation; relay selection; energy saving I. INTRODUCTION In the next-generation wireless communication networs Orthogonal Frequency Division Multiplex Access (OFDMA) has been the main multiple access technology for providing high spectral efficiency through dividing the band into a number of orthogonal subcarriers. This is due to the fact that OFDMA has the ability to combat frequency-selective fading so as to avoid Inter Symbol Interference (ISI) [1]. Since these orthogonal subcarriers can be allocated to different users the resource allocation (subcarrier bit and power) based on OFDMA has a high flexibility and is particularly important for the system performance. Relay technology is able to provide high data rate and extend the high data rate coverage to the edge of cell [-3]. The users having unfavorable channel conditions can select a Relay Station (RS) for forwarding their data information to the Base Station (BS) to maintain the Quality of Service (QoS). Relay is another important technology besides OFDMA Multiple-Input Multiple-Output (MIMO) and smart antenna to expand coverage and improve the transmission rate. Therefore Institute of Electrical and Electronics Engineers (IEEE) 8.16m and the 3rd Generation Partnership Project (3GPP) Advanced Long Term Evolution (LTE-Advanced) have been positioning their efforts towards relay-based architectures in various scenarios. The present wors related to the relay technology is mainly concentrated on the relay selection (to decide whether to use a RS or not and which RS will be selected) [4-5] the relay-enhanced networ architecture (to decide where and how many RSs should be located) the user scheduling scheme in the BS and RS and the OFDMA-based resource allocation in the relay-enhanced cellular networs [6-11]. Ref. Received: 1-7-31 Revised: 1-11-1 Editor: HAO Weimin China Communications April 13 79

To reduce energy consumption in the case of ensuring users quality of service we propose a method to allocate subcarrier bit and power resources for OFDMA relay networs. The optimal problem is solved by integer programming firstly then bandwidth exchange is carried out to let more subcarriers participate in transmission to save energy. Moreover a heuristic energy saving relay selection scheme is proposed. [6] proposed a subcarrier-pair based resource allocation scheme for cooperative multi-relay OFDM systems using Amplify-and-Forward (AF) protocol and its objective is maximizing the system throughput with individual power constraints. To ensure user fairness with minimal impact on system throughput Ref. [7] provides a comprehensive centralized fairnessaware radio resource management algorithm in OFDM cellular relay networs. Ref. [8] developed an optimization framewor to solve the problem of joint relay selection and power allocation using the achievable total rate and max-min user-rate as two figures of merit; however it does not involve subcarrier and bit allocation. The resource allocation strategy in Ref. [9] is based on economic aim to maximize the user satisfaction. It divides the users into two classes: non real-time users and realtime users and prior to satisfy the real-time users requirement. In Ref. [1] they proposed QoS-based resource allocation scheme which can provide higher networ throughput with the ability to satisfy the corresponding QoS requirements. A game theoretic approach is proposed for resource allocation in Ref. [11] and it considers both system capacity and user fairness. All above literatures have no discussion about energy saving radio resource allocation (subcarrier bit power) in relay networs. The response to the global climate change is becoming a major issue related to the national economic and social development. Low carbon economy and energy conservation have become the focus of attention in the world. In wireless relay networs the most part of energy consumption is mainly at BS and RSs sides which are more significant for energy consumption optimization. Therefore different from previous wors we propose a dynamic subcarrier bit and power allocation for the goal of energy saving in the case of ensuring users QoS. The optimal subcarrier bit and power allocation problems which are previously formulated in relay-enhanced networs are reformulated into and solved by integer programming in optimization theory. Moreover according to Shannon s canonical channel capacity formula the channel capacity is only logarithmically dependent on transmit power but nearly linearly dependent on bandwidth [1-13]. In other words we can save a lot of power through the redistribution of small width. Meanwhile in the real system the status of system resource all used is very little; most of the time the system has a surplus of resource. Therefore after resource allocation we carry out the exchange of bandwidth and power to save the total power. In addition relay selection is another important problem in relay-enhanced networs and closely lined with resource allocation. A heuristic energy saving relay selection scheme is proposed in this paper. This paper is organized as follows. In section II system description and assumptions are given. The detail descriptions of proposed resource allocation scheme are given in Section III. Simulation results are presented in Section IV while Section V concludes the paper. II. SYSTEM MODEL In fact the main problem formulation may be different according to different optimization objectives (system throughput maximization or total energy minimization) different relaying protocols (Decode-and-Forward (DF) or AF) different transmission modes (Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD)) and different relaying scenarios (two-hop single-relay two-hop multirelay or linear multi-hop). In this paper our optimization objective is total energy minimization and the relay scenario is DF TDD and two-hop single-relay. The configuration of relay-enhanced networs can be seen in Figure 1. In OFDMAbased systems the frequency domain resource allocation unit is subcarrier and the time domain is sub-frame. In this paper we use TDD mode and one sub-frame is divided into two time slots. The system transport data from BS to RS and BS to Mobile Station (MS) in the first slot while transport data from RS to MS 8 China Communications April 13

in the second slot. The two slots in a sub-frame have the same number of symbols. The system resources can be represented in the form of resource grid as in LTE and IEEE 8.16 standard. Each element in the resource grid which occupied a symbol in the time domain and a subcarrier in the frequency domain is called a Resource Element (RE). In theory RE can be used as the unit of resource allocation each RE with its own Modulation and Coding Scheme (MCS) mode can be assigned to any user. But in practice this will increase the complexity of the resource allocation and reduce the system performance. Therefore in this paper we tae a subcarrier occupying a time slot in the time domain as the unit of resource allocation. Thus each RE can carry different bit data based on different MCS modes. Each RE in the same subcarrier taes the same MCS mode. The detailed sub-frame structure can be seen Figure. We assume that there are K active users in a sub-frame and N subcarriers in each time slot. The least data rate requirement of user is R to maintain the QoS where = {1 K}. As a sub-frame is 1ms for the purpose of calculation the unit of R is bit/ms in this paper. If a user selects one RS to transmit data we denote by relay the RS number selected by user where relay S and S={ 1 N RS } denotes the set of the RSs N RS is the number of RSs in a cell denotes the direct transmission mode between BS and the user. The least number of subcarriers to achieve the required QoS of user can be calculated as R N = {1... K} (1) r where r is the number of information bits that can be transmitted in the center subcarrier with the maximum transmitting power according to user s channel quality and x denoting the smallest integer greater than x. In the next section we select N subcarriers for the th user. In the frequency selective fading channel the same user has different channel conditions in different subcarriers. So that the power required for the same user to transmit the same Fig.1 System model Fig. Sub-frame structure bit information is different in different subcarriers. We denote by α the magnitude of the n channel gain of the nth subcarrier as seen by the th user. We assume that the single-sided noise power spectral density level N is equal to unity for all subcarriers and is the same for all users. To ensure the QoS requirements at the receiving side the transmission power of the th user in the nth subcarrier must be satisfied with SINR( c ) ( N + I ) = () n n n αn P where c n denotes the transmission bit number in the nth subcarrier as seen by the th user and c n D = { 1 L} L is the maxi- China Communications April 13 81

mum number of information bits that can be transmitted by each subcarrier. Signal-to-Interference-and-Noise Radio SINR(x) denotes the lowest SINR requirement for transmitting x bits data according to the MCS table while SINR(x) is a discrete function. I n is the interference of user received from other stations (BSs and RSs) in subcarrier n and it can be described as N n j relay n jn BS _ RS j I =Σ p α (3) where N BS_RS is the number of BSs and RSs in j the system; p is the transmit power of the jth n station in subcarrier n; α is the magnitude j n of the channel gain of the nth subcarrier from station j to user. Using these SINR levels the receiver can demodulate the information according to different MCS modes and achieve the required QoS of all users. Therefore P n is the required power for user to transmit c n bits in subcarrier n. In relay-enhanced networs the system allocates subcarriers to the lin from BS to RS and MS in the first slot and allocates subcarriers to the lin from RS to MS in the second slot. At the BS side for the direct transmission users the consumption power is equal to the power from BS to the user; for the relaying transmission users the consumption power is equal to the power from BS to the RS selected by the user. Therefore SINR( cn ) ( N + In ) relay = αbs MS n Pn = (4) SINR( cn ) ( N + In ) relay α BS RSrelay n where α BS and α MS n BS denote the mag- RS n relay nitude of the channel gain of the nth subcarrier from BS to the th MS and from BS to the relay th RS respectively. At the RS side the system need to allocate subcarriers resource in the second slot for the ith RS SINR( cn ) ( N + In ) i relay = i Pn = αrs {1... } i MS n i NRS (5) relay i where α RS MS n i denotes the magnitude of the channel gain of the nth subcarrier from the ith RS to the th MS. III. ENERGY SAVING RELAY SELECTION AND RESOURCE ALLOCATION (SUBCARRIER BIT AND POWER) SCHEME 3.1 Relay selection In cellular relay networs when a user requires accessing the system the relay selection scheme will choose an access station for the user. Relay selection scheme decides whether to forward data through an RS or not and which RS should be selected by the user. The channel quality of access lin load balance and fairness are all the ey factors impacting relay selection scheme. As the ey objective is energy saving in this paper we propose a heuristic energy saving relay selection scheme. When a user requires accessing to the system the channel situation of different candidate stations to the user is different so the required power is different in order to ensure the user s QoS. The purposed relay selection scheme in this paper is to find the station whose energy consumption is minimal. The relay selection algorithm for user can be described as * i = arg min{ P[SINR( R )]} (6) i S where i * is the minimal power consumption station; R denotes the least data rate requirement of user to maintain its QoS; and BS MS P i = Pi = (7) BS RS RS MS P + P i where BS MS SINR( R) ( N + I) P = αbs MS BS RS SINR( R) ( N + I) P = (8) αbs RSi RS MS SINR( R) ( N + I) P = αrsi MS α BS α MS BS RS i and α RS MS i denote the channel gain in the center subcarrier from BS to i 8 China Communications April 13

MS from BS to the ith RS and from the ith RS to MS respectively. I is the interference of user received from other stations (BSs and RSs) in the center subcarrier. 3. Subcarrier bit and power allocation When a user finishes relay selection it has decided which station it should access. Then the system allocates resources (how many subcarriers which subcarriers how much power and how many bits in each subcarrier) for the user. Our optimization objective is energy saving subject to ensure the users QoS. The optimal subcarrier bit and power allocation problems which are previously formulated in relay-enhanced networs are reformulated into and solved by integer programming in optimization theory. After resource allocation if the system still has a surplus of subcarriers we reallocate the remaining subcarriers using Bandwidth Exchange (BE) to save energy. Mathematically we can formulate the problem of minimizing power as P * N K ρn Pn n= 1= 1 N K cn N + In ρn n= 1= 1 αn = min SINR( ) ( ) = min (9) As we do not allow more than one user to share one subcarrier the minimization is subjected to the constraint ρ n 1 user select subcarrier n = if with ρ n (1) The constraint ensures that each subcarrier can only be used by one user. The least data rate requirement of user R equals the sum of bits in each subcarriers allocated for user. It can be described as N ρ n n {1... } (11) n= 1 R = c K We can use integer programming in Ref. [14] to solve this optimization problem. As we suppose in the Section c n D = { 1 L} SINR(c n ) { SINR(1) SINR(L)} where SINR() = and SINR(c) are constants that can be pre-calculated according to the MCS table. A new indicator variable γ nc is defined as follow γ nc 1 if ρn = 1 and cn = c = (1) otherwise For all c { 1 L} SINR(c n ) is rewritten as L n γ nc c= SINR( c ) = SINR( c) (13) The indicators ρ n and γ nc are related as follows ρ L n γnc c= and it also can be seen that = (14) γ ρ = γ (15) nc n nc Therefore Eq. (9) can be rewritten as P * SINR( c) ( N + I ) = arg min γ nc N K L n γ nc (16) n= 1= 1c= αn In relay-enhanced networs we allocate resource for the BS and the RSs respectively. At the BS side where α P * SINR( c) ( N + I ) = arg min γ nc N K L n γ nc (17) n= 1= 1c= αn = αbs MS n relay n = αbs RSrelay n relay for γ nc { 1} subject to and and (18) N L γ nc = N {1... K} (19) n= 1c= N L γ nc c = R {1... K} () n= 1c= K L γ nc = 1 n {1... N} (1) = 1c= The constraint Eq. (19) ensures that we select N subcarriers for user Eq. () ensures the total bits in the N subcarriers allocated for user equal to the required rate of user and Eq. (1) ensures each subcarrier can only be used by one user. China Communications April 13 83

P i* nc At the RSs side SINR( c) ( N + I ) = arg min γ N K L n γ nc () n= 1= 1c= αrs MS n subject to relay i = i (3) for γ nc { 1} also subject to Eqs. (19-1). Thus the optimization problem can be solved by integer programming when treating γ nc as a variable. Considering the high complexity of integer programming problems as a tradeoff between performance and complexity we can dynamically adjust the cycle of resource allocation according to the existing processer level in practice application. 3.3 Bandwidth exchange To motivate subcarriers reallocation we consider Shannon s canonical channel capacity formula for an Additive White Gaussian Noise (AWGN) channel with a noise power spectral density of N / t P C = W log 1+ NW (4) It is clear that the channel capacity C is only logarithmically dependent on transmit power P t but nearly linearly dependent on bandwidth W especially when W is relatively small. The largest partial derivatives to these variables are respectively given as C 1 C = = N log W t P t P = W= (5) Eq. (5) suggests that incentivising forwarding with additional bandwidth is more promising than using additional transmit power. Refs. [1-13] have discussed about whether it is really beneficial to reallocate resource. The simulation results show that bandwidth exchange can really save energy and improve system capacity. Therefore in this paper after the resource allocation in the previous step if the system still has the remaining subcarriers reallocate the remaining subcarriers one by one. Each subcarrier is assigned to the user that has the most power saving until all the subcarriers allocation completed. The detailed novel energy saving relay selection and resource allocation in OFDMAbased cellular relay networs can be seen in Algorithm 1. Algorithm 1 Step 1: Relay selection. for each user = 1 : K do for each path i = : N RS do Calculate P i in Eq. (7) end for * i = arg mini S { P[ SINR( R )]} i relay =i* end for Step : Subcarrier Bit and Power Allocation. Solve Eq. (17) at the BS side and Eq. () at the RSs side using integer programming in optimization problem. Step 3: Bandwidth Exchange. Reallocate the remaining subcarriers one by one. for each subcarrier n = 1 : N do if mar n = (the no allocation subcarriers) then for each user = 1 : K do Δ P = P P end for = arg max Δ P if Δ > then old new P Allocate subcarrier n to user. end if end if end for IV. SIMULATION RESULTS In this section we validate the proposed energy saving resource allocation algorithm and compare the performance of the proposed algorithm with the greedy algorithm [15]. Greedy algorithm is that when a user arrives it compares all the candidate lins selects the lin of minimum energy per bit and the system allocates the subcarrier of minimum energy per bit to the user one by one. The MCS is set in terms of modulation and code rate and the simulation parameters are listed in Table I and Table II respectively. Figure 3 shows the system total transmission power of one cell in different relay selection and resource allocation schemes. The x-axis is the number of users per cell. In Fig- 84 China Communications April 13

ure 3 proposed energy saving is the proposed energy saving algorithm without the reallocation of the remaining subcarriers (BE Step 3) while proposed energy saving and BE is the completed proposed algorithm. From the figure we can see that the total power increases with the number of users increases and the greedy algorithm has the highest total power to maintain the users QoS while the proposed energy saving and BE have the lowest. We also can see from the figure that when the number of the user is large lie 3 the gap between proposed energy saving without BE and completed proposed algorithm is very small. This is because when the number of the user is large most subcarriers have been allocated and the system has less subcarriers to reallocate. Figure 4 shows the energy efficiency with user number in different algorithms. The energy efficiency is denoted by energy consumption of transmitting one bit data. As seen from the figure greedy algorithm consumes the most energy to transmit a bit data while the proposed energy saving and BE consume the least. Figure 5 shows the spectrum efficiency in different resource allocation algorithms. Spectrum efficiency is the total transmission data rate divided by the subcarriers bandwidths which have been used. The greedy and the proposed energy saving without BE have almost the same spectrum efficiency this is due to the two algorithms which use almost the same number of subcarriers though the subcarriers allocation schemes are different. The completed proposed algorithm reallocates the remaining subcarriers after resource allocation so it uses more subcarriers to transmit the same data and its spectrum efficiency is the least though its energy efficiency is the best. and then we reformulated into and solved the problem by integer programming in optimization theory. Meanwhile we carried out bandwidth Table I Modulation and Coding Scheme (MCS) MCS SINR/dB bit/re QPSK (1/) 5. 1 QPSK (3/4) 8. 1.5 16QAM (1/) 1.5 16QAM (3/4) 14. 3 64QAM (/3) 18. 4 Table II Simulation parameters Parameter Number of cells 19 Number of RSs per cell 3 Number of subcarriers per cell 18 Number of REs per subcarrier 14 Bandwidth of subcarrier Carrier frequency Cell-to-cell distance BS-RS distance (r) BS maximum transmission power RS maximum transmission power Required data rate of each user Path-Loss Lognormal Shadowing 15 KHz 3.5 GHz 1.5 Km Assumption 3/8 of cell-to-cell distance 46 dbm 38 dbm 8 bits/s BS-RS lin Recommendation ITU-R M.15 BS-MS and RS-MS lin BS-RS lin BS-MS and RS-MS lin IEEE 8.16j EVM Type D 3.4 db 8 db V. CONCLUSION In this paper we proposed a dynamic energy saving subcarrier bit and power allocation scheme in OFDMA relay networs. The goal of the proposed algorithm is to save energy to ensure users QoS. We firstly formulated the resource allocation problem in relay networs Fig.3 System total transmit power in different RS selection and resource allocation schemes China Communications April 13 85

that the proposed algorithm consumes less energy to transmit the same bits than the greedy energy saving scheme though its spectrum efficiency is the worst. ACKNOWLEDGEMENT This wor was supported partially by the 973 Program under Grant No. 1CB3161; National Natural Science Foundation of China under Grants No. 617118 No. 613; and the Central Universities Basic Scientific Research Special Fund under Grant No. SWJTU1CX97. References Fig.4 Energy efficiency in different RS selection and resource allocation schemes Fig.5 Spectrum efficiency in different RS selection and resource allocation schemes exchange to let more subcarriers participate in transmission due to most of the time the system is not in full load condition or has free subcarriers. As relay selection scheme is closely lined with resource allocation we also proposed a heuristic relay selection scheme for energy saving and simulated the relay selection scheme joint proposed resource allocation algorithm. Simulation results show [1] SAMPATH H TALWAR S TELLADO J et al. A Fourth-generation MIMO-OFDM Broadband Wireless System: Design Performance and Field Trial Results[J]. IEEE Communications Magazine 4(9): 143-149. [] PABST R WALKE B H SCHULTZ D C et al. Relay-Based Deployment Concepts for Wireless and Mobile Broadband Cellular Radio[J]. IEEE Communications Magazine 4 4(9): 8-89. [3] SALEM M R ADINOYI A B YANIKOMERÖGLU H et al. Opportunities and Challenges in OFDMA-Based Cellular Relay Networs: A Radio Resource Management Perspective[J]. IEEE Transactions on Vehicular Technology 1 59(5): 496-51. [4] GUI B DAI L CIMINI L J J. Selective Relaying in Cooperative OFDM Systems: Two-hop Random Networ[C]// Proceedings of 8 IEEE Wireless Communications and Networing Conference (WCNC 8): March 31-April 3 8. Las Vegas Nevada USA. IEEE Press 8: 996-11. [5] YIN Rui ZHANG Yu ZHANG Jietao et al. Distributed Joint Optimization of Relay Selection and Subchannel Pairing in OFDM Based Relay Networs[C]// Proceedings of 9 IEEE th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 9): September 13-16 9. Toyo Japan. IEEE Press 9: 7-76. [6] DANG Wenbing TAO Meixia MU Hua et al. Subcarrier-Pair Based Resource Allocation for Cooperative Multi-Relay OFDM Systems[J]. IEEE Transactions on Wireless Communications 1 9(5): 164-1649. [7] SALEM M R ADINOYI A B RAHMAN M et al. Fairness-Aware Radio Resource Management 86 China Communications April 13

in Downlin OFDMA Cellular Relay Networs[J]. IEEE Transactions on Wireless Communications 1 9(5): 168-1639. [8] KADLOOR S ADVE R S. Relay Selection and Power Allocation in Cooperative Cellular Networs[J]. IEEE Transactions on Wireless Communications 1 9(5): 1676-1685. [9] WANG Benxu WEN Xiangming SU Dongming et al. User Satisfaction Based Resource Allocation for OFDMA Relay Networs in the Resource-constrained System[C]// Proceedings of 1 IEEE nd International Conference on Future Networs (ICFN 1): January -4 1. Sanya Hainan China. IEEE Press 1: 34-38. [1] CHANG W LIN J FENG K. QoS-based Resource Allocation for Relay-Enhanced OFDMA Networs[C]// Proceedings of 11 IEEE Wireless Communications and Networing Conference (WCNC 11): March 8-31 11. Cancun Mexico. IEEE Press 11: 31-36. [11] PAN Y NIX A R BEACH M E. A Game Theoretic Approach to Distributed Resource Allocation for OFDMA-based Relaying Networs[C]// Proceedings of 8 IEEE 19th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 8): September 15-18 8. Cannes France. IEEE Press 8: 1-5. [1] ZHANG Dan ILERIÖ MANDAYAM N B. Bandwidth Exchange as An Incentive for Relaying[C]// Proceedings of 8 IEEE 4nd Annual Conference on Information Sciences and Systems (CISS 8): March 19-1 8. Princeton NJ USA. IEEE 8: 749-754. [13] ZHANG Dan SHINKUMA R MANDAYAN N B. Bandwidth Exchange: An Energy Conserving Incentive Mechanism for Cooperation[J]. IEEE Transactions on Wireless Communications 1 9(6): 55-65. [14] KIM I H PARK I LEE Y H. Use of Linear Programming for Dynamic Subcarrier and Bit Allocation in Multiuser OFDM[J]. IEEE Transactions on Vehicular Technology 6 55(4): 1195-17. [15] NEGI A SINGH S S. Power Saving Approaches in -hop Relaying Cellular Networs[C]// Proceedings of 5 IEEE 16th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC 5): September 11-14 5. Berlin Germany. IEEE Press 5: 1616-16. Biographies HUANG Bo received the B.S. degree in networ engineering from Southwest Jiaotong University Chengdu China in 6. Now he is a Ph.D. candidate in Key Lab of Information Coding and Transmission Sichuan Province Southwest Jiaotong University. His current research interests include relay selection resource allocation and scheduling in relay-enhanced cellular networs modeling and performance evaluation of next generation cellular networs. Email: bolshuo@163.com FANG Xuming received the B.E. degree in electrical engineering in 1984 the M.E. degree in computer engineering in 1989 and the Ph.D. degree in communication engineering in 1999 from Southwest Jiaotong University Chengdu China. He has to his credit around high-quality research papers in journals and conference publications. He has authored or co-authored five boos or textboos. His research interests include wireless broadband wireless networs multi-hop networs broadband wireless access for high speed railway etc. *The corresponding author. Email: xmfang@ swjtu.edu.cn ZHAO Yue received the B.S. degree in communication engineering in 6 from North China Institute of Science and Technology Langfang China. Since March 8 he has been woring toward his Ph.D. degree with the Department of Communication Engineering Southwest Jiaotong University Chengdu China. His research interests include radio resource allocation modelling and performance evaluation of next generation cellular networs. CHEN Yu received the B.S. degree in 8 in communication engineering from Southwest Jiaotong University Chengdu China. She is currently a Ph.D. candidate at School of Information Science and Technology Southwest Jiaotong University. Her research interests are in wireless communications and multi-hop networ with a current focus on energy efficient wireless relay OFDMA networ and optimization of resource management. HE Rong received the B.S. degree in automation control in 1997 the M.E. degree in traffic information engineering and control in and the Ph.D. degree in computer application technology in 11 from Southwest Jiaotong University Chengdu China. Her research interests include radio resource management access control and schedule scheme of wireless multi-hop networ and mobile Internet. China Communications April 13 87