Reconfigurable Intelligent Surfaces-Enhanced Uplink User-Centric Networks on Energy Efficiency Optimization

User-centric network (UCN) has been considered as a promising technology to improve the throughput of users, especially users at the cell edge, where each user is connected to and served by a group of access points. With the rapid growth of user's uplink traffic demands, energy consumption has become an important issue in uplink UCN as user's battery capacity is limited. To address this issue, in this research, we develop a reconfigurable intelligent surface (RIS)-enhanced uplink UCN, where RIS is exploited to improve the signal quality of uplink transmission for users with virtually no extra energy consumption. To approach an optimal energy efficiency, we jointly perform reflect beamforming at RISs and uplink power control at users. This is formulated into a non-convex and intractable energy efficiency maximization problem. To solve it effectively, we decompose it into two subproblems and carry out the optimization alternately. That is, we design the reflect beamforming matrices at RISs through fractional programming and an uplink power control at users through constructing a surrogate function via majorization-minimization algorithm. Numerical results demonstrate that the proposed algorithm could achieve significant gain both on energy efficiency and spectral efficiency compared to the benchmark algorithm.