Enhanced Fronthaul Capacity in CRANs: Sum-Rate Maximization via Joint Optimal Design of STAR-RIS, Massive MIMO and Data Compression

Abstract

Cloud Radio Access Networks (CRAN) face a critical challenge due to the limited capacity of fronthaul links overwhelmed by massive data transmissions. This paper proposes a novel CRAN design that effectively tackles this challenge. Our approach combines three key elements: (1) Massive MIMO at the baseband unit to leverage large array gain and interference suppression; (2) a novel simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) that can both transmit and reflect signals concurrently, improving fronthaul capacity through energy splitting technique by enabling communication with remote radio heads serving multiple user equipments; and (3) a data compression technique by optimizing the quantization noise covariance matrix across remote radio heads, significantly reducing the fronthaul traffic load. We formulate a problem to maximize the overall network sum-rate by jointly optimizing transmit power, fronthaul capacity, beamforming vectors at RRHs, data compression, and STAR-RIS transmission-reflection coefficients. To address the nonconvexity of the resulting joint optimization problem, successive convexification along with alternating optimization technique are used to develop an iterative algorithm. Simulations demonstrate that our STAR-RIS-aided CRAN design surpasses conventional reflecting-only RIS aided CRAN by providing full-space coverage and thus offering more degrees-of-freedom compared to traditional RIS.