Abstract

In order to solve the problem of spectrum resource shortage and energy consumption, we put forward a new model that combines with D2D communication and energy harvesting technology: energy harvesting-aided D2D communication network under the cognitive radio (EHA-CRD), where the D2D users harvest energy from the base station and the D2D source communicate with D2D destination by D2D relays. Our goals are to investigate the maximization energy efficiency (EE) of the network by joint time allocation and relay selection while taking into the constraints of the signal-to-noise ratio of D2D and the rates of the Cellular users. During this process, the energy collection time and communication time are randomly allocated. The maximization problem of EE can be divided into two sub-problems: (1) relay selection problem; (2) time optimization problem. For the first sub-problem, we propose a weighted sum maximum algorithm to select the best relay. For the last sub-problem, the EE maximization problem is non-convex problem with time. Thus, by using fractional programming theory, we transform it into a standard convex optimization problem, and we propose the optimization iterative algorithm to solve the convex optimization problem for obtaining the optimal solution. And, the simulation results show that the proposed relay selection algorithm and time optimization algorithm are significantly improved compared with the existing algorithms.

中文翻译：

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基于中继选择的D2D通信网络能效资源分配

摘要

为了解决频谱资源短缺和能源消耗的问题，我们提出了一种结合D2D通信和能量收集技术的新模型：认知无线电（EHA-CRD）下的能量收集辅助D2D通信网络，其中D2D用户从基站获取能量，D2D源通过D2D中继与D2D目的地进行通信。我们的目标是通过联合时间分配和中继选择来研究网络的最大能效（EE），同时考虑D2D的信噪比和蜂窝用户速率的约束。在此过程中，将随机分配能量收集时间和通讯时间。EE的最大化问题可以分为两个子问题：（1）中继选择问题；（2）时间优化问题。对于第一个子问题，我们提出了加权和最大算法来选择最佳中继。对于最后一个子问题，EE最大化问题是随时间变化的非凸问题。因此，利用分数规划理论将其转化为标准的凸优化问题，并提出了一种优化迭代算法来解决凸优化问题，以获得最优解。仿真结果表明，与现有算法相比，本文提出的中继选择算法和时间优化算法有了明显的改进。将其转化为标准的凸优化问题，提出了一种优化迭代算法，解决了凸优化问题，得到了最优解。仿真结果表明，与现有算法相比，本文提出的中继选择算法和时间优化算法有了明显的改进。将其转化为标准的凸优化问题，提出了一种优化迭代算法，解决了凸优化问题，得到了最优解。仿真结果表明，与现有算法相比，本文提出的中继选择算法和时间优化算法有明显的改进。