Liping WANG 1, Yusheng JI 1,2, and Fuqiang Liu 3 1 The Graduate University for Advanced Studies, Tokyo, Japan 2 National Institute of Informatics, Tokyo, Japan 3 Dept. of Information and Communication Engineering, Tongji University, Shanghai, China WCNC 2009

Outline  Introduction  System model  Joint Path Selection and Subchannel Allocation  A. Problem Formulation  B. Linearization  C. Joint Path Selection and Subchannel Allocation Algorithm  Performance Evaluation  Conclusions

Introduction  Shadow effect BS RS SS

Introduction BSSSRS Transparent RS Amplify Forward BSSSRS Non-transparent RS Decode Forward Encode

Introduction

 Motivation  In this paper, based on the two-zone frame structure proposed in [10], which supports both relaying and cooperative selection diversity (CSD), we find that the effective data rate of a path on a subchannel used in [5][10] as the path selection rule can only be achieved when the proportion of the two zones equals a optimal value

System model  Each downlink data subframe has a length of Td with the proportion of the DL-access zone and the optimal transparent zone equal to u : (1 − u)  Assumption  Channel states are invariant during one frame  in [10] that one subchannel can be allocated to only one user

A. Problem Formulation B. Linearization C. Joint Path Selection and Subchannel Allocation Algorithm Joint Path Selection and Subchannel Allocation

A. Problem Formulation  f( ・ ) is a nonlinear mapping function that depends on the type of constellation used [11] Achievable data rate Index of a user Index of subchannel Transmission power Channel gain Link

A. Problem Formulation BSSSRS BS RS Direct link First-hop link Second-hop link

A. Problem Formulation  If k receives data directly from the BS on the nth subchannel, its end-to-end achievable data rate on that subchannel is BSSSRS

A. Problem Formulation  if k receives data through the jth RS on n, its end-to-end achievable data rate on that subchannel is BSSSRS FrameFirstSecond BS → RSRS → SS 1/32/3 9bps 3bps 1/3*9=32/3*3=2 FirstSecond BS → RSRS → SS Free

A. Problem Formulation  optimal u that maximizes r j,k,n is equal to BSSSRS FirstSecond BS → RSRS → SS 1/43/4 9bps 3bps u = 3/(9+3) = 3/12 = 1/4 3/4 * 3 = 9/41/4 * 9 = 9/4

A. Problem Formulation  If the subchannel in the first zone or in the second zone cannot be fully occupied FirstSecond BS → RSRS → SS Free FirstSecond BS → RSRS → SS Free

A. Problem Formulation FirstSecond BS → RSRS → SS Free BSSSRS 9bps 3bps f j,k,n = 1/2 – 1/2 * 3/9 = 1/2 – 1/6 =2/6 = 1/3 1/21/31/6 1/6*9=3/21/2*3=3/2 > <

A. Problem Formulation  Void filling algorithm FirstSecond BS → RSRS → SS Free BSSSRS 9bps 3bps 1/21/31/6 1/6*9=3/21/2*3=3/2 BS → SS 1bps

A. Problem Formulation  In many literatures such as [5] and [10], they defined the effective data rate of a two-hop user as 1/(1/9 + 1/3) = 1/(4/9) = 9/4 FirstSecond BS → RSRS → SS 1/43/4 3/4 * 3 = 9/41/4 * 9 = 9/4 BSSSRS 9bps 3bps 1bps

A. Problem Formulation  it is impossible that every relaying path has the same optimal u on every subchannel

A. Problem Formulation  In such a multiuser OFDMA relay system with cooperative selection diversity (CSD), we get the throughput of the kth user as path selection and subchannel allocation indicator (0 or 1)

A. Problem Formulation Minimum number of subchannels required by k r j,k,n is a nonlinear function of p n

B. Linearization  When rate adaptation is adopted, the overall throughput can hardly be reduced even if power allocation is not adaptive [11]

B. Linearization  With fixed power allocation, BS precalculates all r j,k,n values, without the constraint c3, the optimal solution could be easily obtained with the following algorithm

B. Linearization  In this case, the complexity to make the optimal path selection and subchannel allocation is O((J+1)KN). However, if we take c3 into account, the problem has K + N constraints and becomes more complicated. A general linear integer programming has been proven to be NP-complete and can be solved by an exhaustive search, which has a computational complexity of O(((J +1)K) N ) [9]. Suppose there are 6 RSs, 20 users and 128 subchannels in the system, the element in the big O notation is about

C. Joint Path Selection and Subchannel Allocation Algorithm (J+1)K comparisons are needed O(KN log KN) O(3KN ) If we have 6 RSs, 20 users and 128 subchannels, the element in the big O notation equals Compared with , our algorithm reduce the computational complexity considerably.

Performance Evaluation  we assume a widely studied network topology with a BS located in the cell center and uniformly surrounded by certain number of RSs as in [3-6] and [10]  There are totally 128 subchannels

Performance Evaluation  6 RSs are in the cell

Performance Evaluation  u = 0.5

Performance Evaluation  6 RSs are in the cell

Performance Evaluation  u = 0.5

Performance Evaluation  10 RSs are in the cell

Conclusions  In this paper, we formulate the problem on resource allocation for OFDMA relay-enhanced systems.  a heuristic joint path selection and subchannel allocation algorithm is designed and a void filling algorithm is proposed to allocate the remaining resources to users’ direct links.  Simulation results show our path selection rule based on the end-to-end achievable data rate and the void filling algorithm improve the overall throughput especially when the proportion of the two zones is far from the optimal value

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