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首页> 外文期刊>Networks, IET >Joint resource allocation and relay selection via genetic algorithm in multi-user decode-and-forward cooperative systems
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Joint resource allocation and relay selection via genetic algorithm in multi-user decode-and-forward cooperative systems

机译:多用户编解码协作系统中的遗传算法联合资源分配和中继选择

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摘要

This study presents a joint consideration of the relay subset selection, bandwidth allocation and power distribution in multi-user decode-and-forward cooperative networks. The upper bound for the optimisation problem considered is first determined by ignoring some constraints. Thereafter, a genetic algorithm (GA) is addressed to resolve the mixed-integer nonlinear programming problem involved. To accommodate this joint consideration, each chromosome in the proposed GA is divided into an integer string for relay selection, and two real number strings for bandwidth allocation and power distribution. In addition, new crossover and mutation operations are employed for this new type of chromosomes. To alleviate the complexity overhead, a low-complexity two-stage implementation is also addressed. Conducted simulations show that both of the proposed GA and the two-stage implementation can attain close performance as the upper bound and outperform some representative previous works. The two-stage implementation is in particular appealing by exhibiting negligible performance degradation with reduced computations.
机译:这项研究提出了在多用户解码和转发协作网络中中继子集选择,带宽分配和功率分配的共同考虑。首先通过忽略一些约束来确定所考虑的优化问题的上限。此后,解决了遗传算法(GA),以解决所涉及的混合整数非线性规划问题。为了适应这种共同考虑,拟议的遗传算法中的每个染色体都被分为用于中继选择的整数串和用于带宽分配和功率分配的两个实数串。另外,这种新类型的染色体采用了新的交叉和突变操作。为了减轻复杂性开销,还解决了低复杂度的两阶段实现。进行的仿真表明,拟议的遗传算法和两阶段实施都可以达到接近上限的性能,并且性能优于某些以前的代表性工作。两阶段的实现方式特别吸引人,因为在计算量减少的情况下性能下降可忽略不计。

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  • 来源
    《Networks, IET》 |2014年第2期|65-73|共9页
  • 作者单位

    Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan|c|;

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  • 正文语种 eng
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