Compressive Sensing Based User Clustering for Downlink NOMA Systems with Decoding Power

TL;DR

This paper proposes a novel compressive sensing-based user clustering method for downlink NOMA systems that jointly optimizes power control considering both transmission and decoding power, improving power efficiency.

Contribution

It introduces a new joint power control and user clustering framework that incorporates decoding power, solved via an innovative reweighted 1-norm minimization approach.

Findings

Proposed algorithm outperforms traditional matching-based methods.

Effective reduction in total power consumption achieved.

Validated through extensive numerical simulations.

Abstract

This letter investigates joint power control and user clustering for downlink non-orthogonal multiple access systems. Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users. To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints. Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted 1-norm minimization and majorization-minimization. Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.