Maximizing Energy Efficiency for LEO Satellite Systems With Massive MIMO-NOMA

In this paper, we consider the application of Non-Orthogonal Multiple Access (NOMA) in Low Earth Orbit (LEO) satellite communication systems (SCS) with hybrid beamforming structure to support Massive Machine Communication (mMTC). Satellites are typically powered by solar energy, leading to significant energy costs, therefore, we propose a resource allocation scheme aimed at maximizing the energy efficiency of the system. Specifically, we firstly group users based on their channel correlations using the agglomerative nesting (AGNES) algorithm. Subsequently, a joint hybrid beamforming and power allocation problem is formulated to maximize the energy efficiency of the system. To address this non-convex problem with high-dimensional variables, we first obtain the power allocation solution under fixed hybrid beamforming using fractional Programming (FP) and Successive Convex Approximation (SCA) methods. Then, given fixed analog beamforming, we utilize the approximate zero-forcing method to design the digital beamforming. Finally, a Boundary Compressed-Particle Swarm Optimization (BC-PCO) algorithm is used to obtain the analog beamforming. The simulation results demonstrate that the proposed algorithm exhibits significant superiority in terms of energy efficiency.