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谢鲲, 曹建农, 文吉刚. 面向协作通信的最优中继选择和功率分配[J]. 计算机科学技术学报, 2013, 28(2): 343-356. DOI: 10.1007/s11390-013-1335-3
引用本文: 谢鲲, 曹建农, 文吉刚. 面向协作通信的最优中继选择和功率分配[J]. 计算机科学技术学报, 2013, 28(2): 343-356. DOI: 10.1007/s11390-013-1335-3
Kun Xie, Jian-Nong Cao, Ji-Gang Wen. Optimal Relay Assignment and Power Allocation for Cooperative Communications[J]. Journal of Computer Science and Technology, 2013, 28(2): 343-356. DOI: 10.1007/s11390-013-1335-3
Citation: Kun Xie, Jian-Nong Cao, Ji-Gang Wen. Optimal Relay Assignment and Power Allocation for Cooperative Communications[J]. Journal of Computer Science and Technology, 2013, 28(2): 343-356. DOI: 10.1007/s11390-013-1335-3

面向协作通信的最优中继选择和功率分配

Optimal Relay Assignment and Power Allocation for Cooperative Communications

  • 摘要: 无线网络中的协作通信技术可以有效利用空间分集来对抗无线传输衰落, 已得到广泛关注。为了最大化无线协作通信网络中的传输容量, 本文研究联合传输模式选择、中继选择和功率分配的联合优化问题。为了最大化网络容量, 现有的中继选择问题多局限于只存在一个传输节点对的无线网络场景, 因此, 本文所提问题更具挑战性。本文将联合优化问题建模为一个变种的最大化加权匹配问题(VMWMC), 这里的权值定义为依赖功率分配的传输容量函数值。VMWMC是一个non-convex问题, 而且VMWMC问题的复杂性随着中继节点的增加而增加。本文首先证明VMWMC问题具有虚拟的0对偶间隙, 然后基于这个结论, 提出拉格朗日对偶分解的算法求解该问题并降低计算复杂性。仿真实验结果表明, 相比于现有的算法, 本文所提算法可以大大增加网络容量, 而且, 相比于完全搜索的算法, 本文所提算法能获得最优的网络容量的同时还可以大大减少计算量。

     

    Abstract: 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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