Opportunistic cooperation for multi-antenna multi-relay networks

  • Authors:

  • Weiliang Zeng;Chengshan Xiao;Youzheng Wang;Jianhua Lu

  • Affiliations:

  • Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China;Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO;Department of Aeronautics and Astronautics Engineering, Tsinghua University, Beijing, China;Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China

  • Venue:
  • IEEE Transactions on Wireless Communications
  • Year:
  • 2010

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Abstract

A low-complexity, near-optimal transmit antenna selection algorithm is proposed for multi-relay networks where all nodes are equipped with multiple antennas. We first establish a system model and a unified capacity maximization framework for a two-hop opportunistic relaying scheme where the source node (S) transmits signals to multiple relay nodes (R) in the first time slot, and the selected relay antennas and their corresponding relay nodes receive, decode and forward the messages to the destination (D) in the second time slot. Based on the system model, we develop a transmit antenna selection algorithm that maximizes the network capacity assuming that the channel state information is available at the receivers but not available at the transmitters, and total transmit power constraints are imposed on source/relay transmitters. The proposed algorithm first constructs a sorted list of relay antennas with decreasing S-R capacities, then iteratively maximizes the R-D capacity over a candidate antenna set using a low-complexity, near-optimal antenna selection scheme. The candidate set is reduced in the next iteration according to the selected antenna set of the current iteration. The overall network capacity is computed for the selected antenna sets of all iterations, and the set yielding the highest S-R-D capacity is the solution to the maximization problem. We show that this novel iterative algorithm achieves near-optimal solution and has a polynomialtime complexity. We also derive the lower and upper bounds of the achievable network capacity for both average capacity and outage capacity. Numerical examples show the significant performance gains obtained via the proposed scheme compared to its conventional counterparts.