Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach

In multicell massive multiple-input-multiple-output (MIMO) nonorthogonal multiple access (NOMA) networks, base stations with multiple antennas deliver their radio-frequency energy in the downlink, and Internet-of-Things devices use their harvested energy to support uplink data transmission. This article investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer. To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information are considered and their corresponding EE performance is analyzed. Under the Quality-of-Service requirements, an EE maximization problem is formulated, which is nontrivial due to nonconvexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex and then develop a distributed alternating direction method of multipliers-based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.