In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RISassisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature.
翻译:近年来,随着数据流量的爆炸性增长,通信基站(BS)需要为更多用户提供服务。下行流量从BS中卸载已成为减轻BS负担的有效方式。设备对设备(D2D)通信因其可以通过再利用蜂窝频段频谱来提高频段利用率而应运而生。在一般环境中,出现了包括毫米波(mm-wave)在内的异构蜂窝网络(HCN)。由于D2D链接允许与蜂窝用户共享频谱资源,因此它将对蜂窝用户带来潜在的干扰。幸运的是,一种名为可重构智能表面(RIS)的新兴技术,可以通过塑造入射波束和改善多径相移来缓解D2D链接引起的严重干扰。本文研究了在RIS辅助下的单一蜂窝异构D2D通信场景中实现系统总速率最大化的资源分配方案。为解决块坐标下降(BCD)问题,将最大化总速率问题分解为三个子问题。资源分配子问题由基于博弈论的合作联盟方法解决。联盟的功率分配问题通过数学变换将凹函数转化为凸优化。该问题采用梯度下降法求解。采用局部搜索方法找到相位转换问题的最优解,然后迭代直到速率差小于阈值。仿真结果表明,所设计的算法优于文献中的其他基准方案。