Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach

TL;DR

This paper proposes a DDPG-based reinforcement learning method to optimize downlink C-NOMA transmission with movable antennas, jointly adjusting beamforming, power, and antenna positions for enhanced sum rate performance.

Contribution

It introduces a novel joint optimization framework for beamforming, power, and antenna positioning in C-NOMA with movable antennas using reinforcement learning.

Findings

Achieves up to 45% performance gain over benchmark schemes.

Demonstrates 93% accuracy compared to the optimal solution.

Shows significant improvements with movable antennas in C-NOMA systems.

Abstract

This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be able to transmit the signal to the other user providing an extra transmission resource and enhancing performance. Both users are equipped with a receiving MA each and a transmitting MA for the relaying user. In this regard, we formulate an optimization problem with the objective of maximizing the achievable sum rate by jointly determining the beamforming vector at the BS, the transmit power at the device and the positions of the MAs while meeting the quality of service (QoS) constraints. Due to the non-convex structure of the formulated problem and the randomness in the channels we adopt a deep deterministic policy gradient (DDPG) approach, a reinforcement learning (RL) algorithm capable of dealing with continuous state and action spaces. Numerical results demonstrate the superiority of the presented model compared to the other benchmark schemes showing gains reaching 45% compared to the NOMA enabled MA scheme and 60% compared to C-NOMA model with fixed antennas. The solution approach showed 93% accuracy compared to the optimal solution.