A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks

TL;DR

This paper introduces a deep SARSA(λ) reinforcement learning approach to optimize uplink resource allocation in NOMA-URLLC networks, improving reliability and reducing decoding errors in dynamic environments.

Contribution

It presents a novel deep SARSA(λ) algorithm tailored for resource sharing in NOMA-URLLC, addressing user clustering, feedback, and optimal allocation challenges.

Findings

Converges within 200 episodes to low mean error

Outperforms traditional Q-learning in decoding error probability

Efficient feedback system enhances long-term learning

Abstract

In this paper, we propose a deep state-action-reward-state-action (SARSA) $\lambda$ learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. Lastly, we compare the performance of the proposed algorithm with conventional Q-learning and SARSA Q-learning algorithms. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithms, the proposed solution is able to converges within \myb{200} episodes for providing as low as $10^{-2}$ long-term mean error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The proposed feedback system is efficient for the long-term learning process.