Energy-Efficiency Optimization for Multiple Access in NOMA-Enabled Space-Air-Ground Networks

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space-air-ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the nonorthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low-earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), power allocation (PA), and UAV 3-D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3-D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

Automated summary

This work investigates the uplink transmission in a space-air-ground (SAG) communication network, aiming to maximize the energy efficiency through optimizing user association, power allocation, and UAV trajectory. The joint UA, PA, and UAV trajectory optimization algorithm is developed using the block coordinate descent method. Numerical results demonstrate the superiority of the proposed algorithm in terms of energy efficiency and convergence speed.