NOMA Assisted Downlink Power Allocation in Pinching Antenna Systems Using Convolutional Neural Network

TL;DR

This paper introduces a CNN-based method for optimizing antenna placement and power allocation in a novel pinching-antenna NOMA system, enhancing performance and fairness with reduced computational effort.

Contribution

It proposes a two-stage optimization and a CNN framework for scalable, near-optimal power allocation in PA-assisted NOMA systems, addressing placement and resource management.

Findings

Improved sum rate and user fairness in simulations

Reduced computational complexity compared to traditional methods

Effective inference of power allocation for unseen configurations

Abstract

In this paper, we consider a flexible-antenna architecture, referred to as a pinching-antenna (PA) system, in which multiple PAs realized by activating small dielectric particles along a dielectric waveguide are jointly employed to serve a single-antenna user. We investigate antenna placement and power allocation optimization in PA-assisted non-orthogonal multiple access (NOMA) systems using a convolutional neural network (CNN). An optimization strategy is developed to determine the PA locations that maximize achievable NOMA performance while satisfying physical and spatial constraints. The proposed method adopts a two-stage structure, combining a user-aware initialization with a gradient-based refinement, enabling near-optimal performance with significantly lower computational cost. A max-min fairness formulation is introduced for power allocation to balance the power budget among users with varying channel strengths, solved efficiently via quasi-linear programming and bisection search. Finally, a CNN-based learning framework is employed to capture the nonlinear mapping between channel conditions and the corresponding optimal power coefficients. This framework can infer near-optimal power allocations for unseen network configurations without retraining, offering scalability and adaptability. Simulation results show that the proposed CNN-based NOMA approach for PA systems improves sum rate and user fairness while reducing computational complexity.