Towards Optimal Resource Allocation in Wireless Powered Communication Networks with Non-Orthogonal Multiple Access

TL;DR

This paper investigates optimal resource allocation in Wireless Powered Communication Networks using NOMA, balancing throughput maximization and fairness through convex approximation methods for complex optimization problems.

Contribution

It introduces a convex approximation approach to solve non-convex resource allocation problems in NOMA-based WPCNs, considering both sum-throughput and fairness objectives.

Findings

Trade-off between sum throughput and fairness.

Convex approximation effectively solves non-convex optimization.

Different decoding schemes impact resource allocation strategies.

Abstract

The optimal allocation of time and energy resources is characterized in a Wireless Powered Communication Network (WPCN) with non-Orthogonal Multiple Access (NOMA). We consider two different formulations; in the first one (max-sum), the sum-throughput of all users is maximized. In the second one (max-min), and targeting fairness among users, we consider maximizing the min-throughput of all users. Under the above two formulations, two NOMA decoding schemes are studied, namely, low complexity decoding (LCD) and successive interference cancellation decoding (SICD). Due to the non-convexity of three of the studied optimization problems, we consider an approximation approach, in which the non-convex optimization problem is approximated by a convex optimization problem, which satisfies all the constraints of the original problem. The approximated convex optimization problem can then be solved iteratively. The results show a trade-off between maximizing the sum throughout and achieving fairness through maximizing the minimum throughput.