Optimum Fairness for Non-Orthogonal Multiple Access

TL;DR

This paper addresses fairness in NOMA by deriving a closed-form optimal solution for maximizing the worst user's rate, utilizing Perron-Frobenius theory, and proposing an efficient iterative algorithm.

Contribution

It introduces a novel closed-form solution for the fairness optimization in NOMA and an efficient iterative algorithm with linear convergence.

Findings

Closed-form optimal value and solution derived

Proposed algorithm converges linearly and is more efficient

Enhances fairness optimization in NOMA systems

Abstract

This paper focuses on the fairness issue in non-orthogonal multiple access (NOMA) and investigates the optimization problem that maximizes the worst user's achievable rate. Unlike previous studies, we derive a closed-form expression for the optimal value and solution, which are related to Perron-Frobenius eigenvalue and eigenvector of a defined positive matrix. On this basis, we propose an iterative algorithm to compute the optimal solution, which has linear convergence and requires only about half iterations of the classical bisection method.