IRS-Assisted Downlink OFDMA in C-RAN With Coordinated DF-RRH and DCF-RRH
Yu Zhang, Guangshan Cheng, Huadong Duan, Hong Peng, Weidang Lu · IEEE Transactions on Green Communications and Networking · 2025
This paper investigates a downlink orthogonal frequency division multiple access (OFDMA) cloud radio access network (C-RAN), where the intelligent reflecting surfaces (IRSs) are exploited to enhance the communication between the remote radio heads (RRHs) and users. Two types of RRHs are deployed, namely Decode-and-Forward-RRH (DF-RRH) and Decompress-and-Forward-RRH (DCF-RRH), corresponding to two typical functional splitting configurations between the baseband unit (BBU) pool and RRH. Due to the limited capacity of the fronthauls, the BBU pool selectively forwards the messages on part of subcarriers (SCs) to each DF-RRH while for the DCF-RRH, the fronthaul compression is conducted. To maximize the system downlink rate, we propose a joint design of the RRH beamforming, the IRS beamforming, the SC-user assignments, the SC-selection for the DF-RRHs and the fronthaul compression for the DCF-RRHs, under the ideal IRS reflection model. The problem is highly-coupled and involves integer variables. We efficiently solve it by adopting the successive convex approximation (SCA) approach and then using Lagrange duality method and semi-definite relaxation (SDR). Based on this, we further develop a joint design scheme under a practical IRS reflection model. Simulation results demonstrate the effectiveness of the proposed schemes for both the ideal and practical IRS reflection models.