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GA based optimal resource allocation and user matching in device to device underlaying network

机译:设备之间到设备底层网络中基于遗传算法的最佳资源分配和用户匹配

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Device-to-Device (D2D) communication is expected to play a pivotal role in the next generation of LET/LTE-A network, which is able to improve the system throughput by reusing the cellular resource. However, the caused intra-cell interference is quite a challenging issue in D2D underlaying cellular network. In this paper, we propose a Genetic Algorithm (GA) based joint resource allocation and user matching scheme (GAAM) to minimize the intra-cell interference while maximize the system throughput. With the proposed scheme, we firstly analyze and formulate the user matching scheme in mathematical model, during which resources are allocated to cellular user equipments (CUE) and further be shared by D2D UEs (DUE) in a coordinated manner. Secondly, GA is used to globally search the optimal user matching solution to maximize the system throughput. Furthermore, with some innovations, the more efficient GAAM can be applied to a much wider scope in D2D underlying network. Simulation results show that GAAM achieves nearly the same system throughput comparing to the exhaustive method, which is 30 Mbps bigger than the traditional greedy algorithm based allocation and matching scheme (GreedyAM).
机译:预计设备到设备(D2D)通信将在下一代LET / LTE-A网络中发挥关键作用,该网络能够通过重用蜂窝资源来提高系统吞吐量。然而,在D2D底层蜂窝网络中,引起的小区内干扰是相当具有挑战性的问题。在本文中,我们提出了一种基于遗传算法(GA)的联合资源分配和用户匹配方案(GAAM),以最小化小区内干扰,同时最大化系统吞吐量。借助提出的方案,我们首先在数学模型中分析和制定了用户匹配方案,在此期间,资源被分配给蜂窝用户设备(CUE),并由D2D UE(DUE)以协调的方式共享。其次,GA用于全局搜索最佳用户匹配解决方案,以最大化系统吞吐量。此外,通过一些创新,更有效的GAAM可以应用于D2D基础网络中更广泛的范围。仿真结果表明,与穷举法相比,GAAM实现了几乎相同的系统吞吐量,比传统的基于贪婪算法的分配和匹配方案(GreedyAM)大30 Mbps。

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