Energy-Constrained UAV Data Collection Systems: NOMA and OMA

TL;DR

This paper compares NOMA and OMA schemes for UAV data collection, proposing algorithms to maximize minimum throughput under energy constraints, and finds NOMA outperforms OMA with sufficient energy at ground nodes.

Contribution

It introduces novel algorithms for optimizing NOMA and OMA in energy-constrained UAV data collection systems, addressing decoding order and throughput maximization.

Findings

NOMA outperforms OMA when ground nodes have sufficient energy.

Proposed algorithms improve max-min throughput compared to benchmarks.

Efficient AO and SCA techniques are effective for the optimization problems.

Abstract

This paper investigates unmanned aerial vehicle (UAV) data collection systems with different multiple access schemes, where a rotary-wing UAV is dispatched to collect data from multiple ground nodes (GNs). Our goal is to maximize the minimum UAV data collection throughput from GNs for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission, subject to the energy budgets at both the UAV and GNs, namely \emph{double energy limitations}. 1) For OMA, we propose an efficient algorithm by invoking alternating optimization (AO) method, where each subproblem is alternately solved by applying successive convex approximation (SCA) technique. 2) For NOMA, we first handle subproblems with fixed decoding order using SCA technique. Then, we develop a penalty-based algorithm to solve the decoding order design subproblem. Numerical results show that: i) The proposed algorithms are capable of improving the max-min throughput performance compared with other benchmark schemes; and ii) NOMA yields a higher performance gain than OMA when GNs have sufficient energy.