Resource Allocation in Multi-channel Multi-user Relay System with Fairness Constraints

  • Authors:

  • Rui Yin;Yu Zhang;Guanding Yu;Zhaoyang Zhang;Jietao Zhang

  • Affiliations:

  • Institute of Information and Communication Engineering, Zhejiang University, Hangzhou, China;Institute of Information and Communication Engineering, Zhejiang University, Hangzhou, China;Institute of Information and Communication Engineering, Zhejiang University, Hangzhou, China;Institute of Information and Communication Engineering, Zhejiang University, Hangzhou, China;Wireless Research Department, Huawei Technologies Co., Ltd., Shenzhen, China 518129

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2012

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Abstract

In this paper, we derive optimal joint power allocation, subchannel pairing and scheduling strategies in multiple orthogonal channels multiple users wireless networks in the presence of a single regenerative relay node. Two models with users' data rate request (fairness) constraint in different time domains are considered. The first one is called deterministic model in which each user's data rate request has to be satisfied in each time slot t (named short term fairness constraint) and the second one is called stochastic model in which users have average data rate request (named long term fairness). In these two models the optimization problems of maximizing system capacity with total transmit power constraint and fairness constraint are formulated. The Lagrangian dual method is used to derive the optimal solution for deterministic model and in the stochastic model stochastic approximation and dual method are employed to find out the optimal algorithm. Both algorithms have polynomial times complexity, which is reduced significantly compared with the Exhaustive Search Method (ESM). Since Lagrangian dual method is utilized in both schemes, the dual gap is also analyzed. Furthermore, through the analysis and simulation, we see that the optimal resource allocation algorithm in stochastic model has better performance than that in the deterministic model for its ability to exploit temporal diversity.