Joint AP clustering and beamforming in IRS-aided User-Centric Networks

User-centric network is regarded as a promising technology for future mobile networks to achieve high quality-of-service (QoS) requirements. However, with the massive deployment of access points (APs), power consumption and interference management have become challenging tasks. To address the problem, we explore an intelligent reflecting surface (IRS) aided downlink user-centric network, utilizing IRSs to reduce power consumption and improve system performance. Considering user mobility and the effect of IRSs on the channel, we formulate a time-varying sum rate maximization problem and propose an effective alternating optimization algorithm that jointly optimizes the passive beamforming at IRSs, active beamforming at APs, and AP clustering. As for the proposed algorithm, AP clustering is solved using the discrete particle swarm optimization and beamforming is optimized based on the fractional programming method. Our simulation results show that the proposed algorithm can achieve significant sum rate improvement compared with baseline algorithms.