D2D和大规模MIMO共存时的分布式吞吐量和节能资源优化

Abi Abate Dejen;Yihenew Wondie;Anna Förster
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摘要

第五代(5G)蜂窝网络旨在克服动态服务质量要求带来的挑战性需求,这是单一网络技术无法实现的。未来的蜂窝网络需要高效的资源分配和功率控制方案,以满足多种技术共存和共享网络资源时对吞吐量和能效的要求。在本文中,我们优化了吞吐量和能效(EE)性能,以实现已确定用于未来蜂窝网络的两种技术的共存,即大规模多输入多输出(MIMO)和网络辅助设备对设备(D2D)通信。在这种混合网络中,频谱共享引起的蜂窝和D2D通信之间的co/跨层干扰是一个重大挑战。为此,我们将平均和速率和EE优化问题表述为混合整数非线性规划(MINLP)。我们开发了基于匹配理论的分布式资源分配算法,以减轻干扰和优化网络性能。结果表明,所提算法收敛于稳定匹配,并在有限迭代后终止。matlab仿真结果表明,所提算法在较低复杂度的优化匹配中,平均传输速率达到88%以上,能效性能达到86%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist
Fifth generation (5G) cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements, which are not achieved by single network technology. The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources. In this paper, we optimize the throughput and energy efficiency (EE) performance for the coexistence of two technologies that have been identified for the future cellular networks, namely, massive multiple-input multiple-output (MIMO) and network-assisted device-to-device (D2D) communications. In such a hybrid network, the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge. To this end, we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming (MINLP). We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance. It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations. Mat-lab simulation results show that the proposed algorithms achieved more than 88% of the average transmission rate and 86% of the energy efficiency performance of the optimal matching with lower complexity.
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