An efficient relay selection strategy for high speed users in cooperative diversity systems
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Cooperative resource management in cognitive WiMAX with femto cells
INFOCOM'10 Proceedings of the 29th conference on Information communications
Performance analysis and power allocation for M-QAM cooperative diversity systems
IEEE Transactions on Wireless Communications
Distributed Matching Schemes for Multi-source and Multi-relay Cooperative Wireless Networks
Wireless Personal Communications: An International Journal
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We are concerned with optimally grouping active mobile users in a two-user-based cooperative diversity system to maximize the cooperative diversity energy gain in a radio cell. The optimization problem is formulated as a non-bipartite weighted-matching problem in a static network setting. The weighted-matching problem can be solved using maximum weighted (MW) matching algorithm in polynomial time O(n3). To reduce the implementation and computational complexity, we develop a Worst-Link-First (WLF) matching algorithm, which gives the user with the worse channel condition and the higher energy consumption rate a higher priority to choose its partner. The computational complexity of the proposed WLF algorithm is O(n) while the achieved average energy gain is only slightly lower than that of the optimal maximum weighted- matching algorithm and similar to that of the 1/2-approximation Greedy matching algorithm (with computational complexity of O(n2 log n)) for a static-user network. We further investigate the optimal matching problem in mobile networks. By intelligently applying user mobility information in the matching algorithm, high cooperative diversity energy gain with moderate overhead is possible. In mobile networks, the proposed WLF matching algorithm, being less complex than the MW and the Greedy matching algorithms, yields performance characteristics close to those of the MW matching algorithm and better than the Greedy matching algorithm.