Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks

TL;DR

This paper proposes a joint design of fronthaul compression and passive beamforming in IRS-assisted C-RANs to enhance uplink sum rate, using advanced optimization techniques.

Contribution

It introduces a novel joint optimization framework for fronthaul compression and IRS passive beamforming in IRS-assisted C-RANs, employing Arimoto-Blahut and SDR methods.

Findings

The proposed algorithm improves uplink sum rate performance.

Numerical results demonstrate the effectiveness of the joint design.

Performance gains are achieved compared to baseline methods.

Abstract

This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH). Specifically, we consider the uplink transmission where each RRH quantizes the received signals from the users by either point-to-point compression or Wyner-Ziv compression and then transmits the quantization bits to the BBU pool through capacity limited fronthhual links. To maximize the uplink sum rate, we jointly optimize the passive beamformers of IRSs and the quantization noise covariance matrices of fronthoul compression. An joint fronthaul compression and passive beamforming design is proposed by exploiting the Arimoto-Blahut algorithm and semidefinte relaxation (SDR). Numerical results show the performance gain achieved by the proposed algorithm.