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Joint subcarrier pairings and power allocations with interference management in cognitive relay networks based on genetic algorithms

机译:基于遗传算法的认知中继网络中具有干扰管理的联合副载波配对和功率分配

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This paper considers the resource allocation for cognitive decode-and-forward (DF) relay networks, where a cognitive radio (CR) can access the spectrum under the coexistence constraints. This CR user is assumed to adopt the celebrated orthogonal frequency division multiplexing (OFDM) technique. Our objective is to maximize the rate of the CR user by appropriate power allocation and subcarrier pairing under the individual power constrains at the source and the relay; while the interference to the primary user (PU) must be kept below a target. To resolve the corresponding mixed integer programming (MIP) problem with reasonable cost, new heterogeneous genetic algorithms (HGA) with initialization methods inspired by convex optimization are proposed. To further reduce complexity, an efficient two-stage implementation of the HGA is also presented. Conducted simulations show that our HGA provides superior performance to previous works.
机译:本文考虑了认知解码转发(DF)中继网络的资源分配,其中认知无线电(CR)可以在共存约束下访问频谱。假定此CR用户采用著名的正交频分复用(OFDM)技术。我们的目标是通过在源和中继的单个功率约束下通过适当的功率分配和子载波配对来最大化CR用户的速率。而对主要用户(PU)的干扰必须保持在目标以下。为了以合理的成本解决相应的混合整数规划(MIP)问题,提出了一种新的具有凸优化启发式初始化方法的异质遗传算法(HGA)。为了进一步降低复杂性,还提出了HGA的高效两阶段实施方案。进行的仿真表明,我们的HGA具有比以前的作品更高的性能。

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