ChinaCom2008-Wireless Communications and Networking Symposium

Research Article

Joint Optimization of Power Control, Channel Assignment and Scheduling in Wireless Mesh Assignment and Scheduling in Wireless Mesh

  • @INPROCEEDINGS{10.1109/CHINACOM.2008.4685100,
        author={Xun Chen and Zhaoyang Zhang and Haiyan Luo},
        title={Joint Optimization of Power Control, Channel Assignment and Scheduling in Wireless Mesh Assignment and Scheduling in Wireless Mesh},
        proceedings={ChinaCom2008-Wireless Communications and Networking Symposium},
        publisher={IEEE},
        proceedings_a={CHINACOM2008-WCN},
        year={2008},
        month={11},
        keywords={wireless mesh network power control channel assignment scheduling joint optimization},
        doi={10.1109/CHINACOM.2008.4685100}
    }
    
  • Xun Chen
    Zhaoyang Zhang
    Haiyan Luo
    Year: 2008
    Joint Optimization of Power Control, Channel Assignment and Scheduling in Wireless Mesh Assignment and Scheduling in Wireless Mesh
    CHINACOM2008-WCN
    IEEE
    DOI: 10.1109/CHINACOM.2008.4685100
Xun Chen1,*, Zhaoyang Zhang2,*, Haiyan Luo1,*
  • 1: Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China
  • 2: Institute of Information and Communication Engineering, Zhejiang University, Hangzhou 310027, China , Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou, 310027, China
*Contact email: sky-sky-sky-rush@tom.com, ning_ming@zju.edu.cn, luohy@zju.edu.cn

Abstract

The optimization problem of maximizing the throughput of a regular multi-radio, multi-channel wireless mesh network is addressed in this paper. Given certain network topology and traffic demands, by jointly considering power control, channel assignment and scheduling, we study how to maximize the network throughput under fairness constraint. Based on graph theory, the problem is divided into several sub-problems, which can be formulated as linear programming. It is proved that the solutions of these sub-problems will lead to the global optimal solution to our problem. However, the computational complexity of the above algorithm is O(2^n) as it needs to traverse all possible scenarios. Therefore, a suboptimal algorithm is proposed to achieve a complexity of O(n) at the cost of moderate performance degradation. Simulation results show that the suboptimal algorithm solves the problem much faster, while the performance degradation is less than 8%, in comparison with the optimal one.