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A Distributed Approach to Improving Spectral Efficiency in Uplink Device-to-Device-Enabled Cloud Radio Access Networks

机译:一种分布式方法,可提高启用了设备到设备的上行链路云无线电接入网络的频谱效率

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摘要

Device-to-device (D2D)-enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two users. However, due to the need to acquire global channel state information (CSI) and to execute centralized algorithms, heavy burdens are placed on the fronthaul and the baseband unit (BBU) pool. To alleviate these burdens, a distributed approach to mode selection and resource allocation for potential D2D pairs under pre-determined resource allocation of C-RAN users is proposed, in which pairs of users are endowed with decision-making capabilities. The proposed procedure is divided into three stages: communication mode and subchannel selection, utility value determination, and reinforcement-learning-based strategy update. The core idea is that the D2D pairs self-optimize the mode selection and resource allocation without global CSI under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the impacts of the fronthaul capacity, the centralized signal processing capability of the BBU pool, and the distance between the D2D transmitter and the remote radio head are demonstrated and analyzed.
机译:支持设备到设备(D2D)的云无线电接入网络(C-RAN)是潜在的解决方案,可通过允许两个用户之间的直接通信来进一步提高频谱效率(SE)和减少延迟。但是,由于需要获取全局信道状态信息(CSI)并执行集中式算法,因此,在前传和基带单元(BBU)池上放置了沉重的负担。为了减轻这些负担,提出了在C-RAN用户的预定资源分配下对潜在的D2D对进行模式选择和资源分配的分布式方法,其中赋予了用户对决策能力。该程序分为三个阶段:通信模式和子通道选择,效用值确定以及基于强化学习的策略更新。核心思想是D2D对在一些实际约束下无需全局CSI即可自动优化模式选择和资源分配。仿真结果表明,启用D2D可以显着改善C-RAN的SE。此外,还演示并分析了前传容量,BBU池的集中信号处理能力以及D2D发射机与远程无线电头之间的距离的影响。

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