Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Abstract

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

摘要

得益于现在的中央信息处理和资源管理能力,对于智简化的第六代(6G)无线网络,云无线接入网(C-RAN)是一种很有前景的网络结构。然而,为了进一步增强云无线接入网的容量和覆盖范围,需要部署更多的无线射频拉远头(RRH)以及高保真、低延迟的前传链路,这会导致较高的实施成本。为了解决这个问题,本文提出利用智能反射面(IRS)作为增强云无线接入网低成本且节能的替代方法。具体来说,我们考虑多天线用户通过多天线射频拉远头与基带单元(BBU)池上行通信,并且在用户和射频拉远头之间部署多个智能反射面。射频拉远头可进行点对点压缩或Wyner-Ziv编码来压缩接收信号,然后通过前传链路转发到基带单元池。研究了在前传链路容量受约束情况下,对用户的发送波束成形、智能发射面的被动波束成形和前传压缩噪声的协方差矩阵进行联合优化,以在点对点或者Wyner-Ziv编码压缩下最大化上行总速率。通过利用Arimoto-Blahut算法和半正定松弛(SDR),提出一种连续凸近似方法解决上述非凸问题,并提供两种分别对应于点对点压缩和Wyner-Ziv编码的迭代算法。数值仿真结果验证了在云无线接入网中部署智能反射面带来的性能增益以及所提联合设计的优势。