›› 2013, Vol. 28 ›› Issue (2): 343-356.doi: 10.1007/s11390-013-1335-3

• Computer Network • Previous Articles     Next Articles

Optimal Relay Assignment and Power Allocation for Cooperative Communications

Kun Xie1 (谢鲲), Member, CCF, Jian-Nong Cao2 (曹建农), Senior Member, CCF, IEEE, Member, ACM and Ji-Gang Wen3 (文吉刚)   

  1. 1 School of Information Science and Engineering, Hunan University, Changsha 410082, China;
    2 Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, China;
    3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2012-01-15 Revised:2012-07-12 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    The work is supported by the National Basic Research 973 Program of China under Grant No. 2012CB315801, the National Natural Science Foundation of China under Grant Nos. 61133015, 61003305, 61173167, and the Ph.D. Programs Foundation of Ministry of Education of China under Grant No. 20100161120022.

Cooperative communication for wireless networks has gained a lot of recent interest due to its ability to mitigate fading with exploration of spatial diversity. In this paper, we study a joint optimization problem of jointly considering transmission mode selection, relay assignment and power allocation to maximize the capacity of the network through cooperative wireless communications. This problem is much more challenging than relay assignment considered in literature work which simply targets to maximize the transmission capacity for a single transmission pair. We formulate the problem as a variation of the maximum weight matching problem where the weight is a function over power values which must meet power constraints (VMWMC). Although VMWMC is a non-convex problem whose complexity increases exponentially with the number of relay nodes, we show that the duality gap of VMWMC is virtual zero. Based on this result, we propose a solution using Lagrange dual decomposition to reduce the computation complexity. We do simulations to evaluate the performance of the proposed solution. The results show that our solution can achieve maximum network capacity with much less computation time compared with exhaustive search, and our solution outperforms existing sub-optimal solutions that can only achieve much lower network capacity.

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