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首页> 外文期刊>Procedia Computer Science >Power and Spectrum Allocation in D2D Networks Based on Coloring and Chaos Genetic Algorithm
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Power and Spectrum Allocation in D2D Networks Based on Coloring and Chaos Genetic Algorithm

机译：基于着色和混沌遗传算法的D2D网络功率和频谱分配

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摘要

To tackle the current issue of spectrum resource scarcity, Device-to-Device (D2D) communication is considered as an important technology for sharing spectrum with cellular users, which enables high-speed and convenient services. In this paper, we consider the spectrum and power allocation problem for D2D communication in underlaying cellular network. In particular, we focus on a D2D network, where spectrum resource is shared among all D2D users. Unlike existing works that mainly focus onD2D users sharing spectrum with cellular users, we analyze not only the spectrum resources allocation but also the optimization of the transmission power for each D2D transceiver pair to maximize D2D system capacity maximum. We first formulate the network model and signal-to-interference-plus-noise ratio (SINR) by some stochastic geometry knowledges. In addition, we combine the spectrum resource distribution with power selection and transform this problem into a mixed integer non-linear program(MINLP) to maximize system capacity. Inspired by existing intelligent algorithms, we propose a heuristic chaos genetic algorithm associated with four color theorem to solve this problem. In order to prove the feasibility and efficiency of this algorithm, we compare this problem with brute-force search algorithm in simulation results. It shows that the proposed chaos genetic algorithm combine with graph coloring method can approach system capacity optimal but algorithm complexity is greatly reduced. We also verify the system parameter influence of the system capacity in this network.
机译：为了解决当前频谱资源稀缺的问题，设备到设备（D2D）通信被认为是与蜂窝用户共享频谱的重要技术，可实现高速便捷的服务。在本文中，我们考虑了底层蜂窝网络中D2D通信的频谱和功率分配问题。特别是，我们专注于D2D网络，其中所有D2D用户之间共享频谱资源。与现有的工作主要集中于与蜂窝用户共享频谱的D2D用户不同，我们不仅分析频谱资源分配，而且还分析每个D2D收发器对的传输功率的优化，以最大程度地最大化D2D系统容量。我们首先通过一些随机的几何知识来制定网络模型和信干噪比（SINR）。此外，我们将频谱资源分配与功率选择相结合，并将此问题转换为混合整数非线性程序（MINLP），以最大化系统容量。在现有智能算法的启发下，我们提出了一种与四色定理关联的启发式混沌遗传算法来解决该问题。为了证明该算法的可行性和有效性，我们在仿真结果中将该问题与蛮力搜索算法进行了比较。结果表明，所提出的混沌遗传算法与图着色方法相结合可以达到系统容量最优的目的，但是大大降低了算法的复杂度。我们还验证了该网络中系统参数对系统容量的影响。

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