Q-Learning-based Resource Allocation with Priority-based Clustering for Heterogeneous NOMA Systems

Abstract

The fifth-generation (5G) network is meant to support enhanced mobile broadband (eMBB), ultra-reliable and low-latency communication (URLLC), and massive machine-type communication (mMTC) services. With the development of the 5G network Non-orthogonal multiple access (NOMA) technique is getting popular due to its spectral efficiency, high reliability, and massive connectivity support. To make the NOMA more efficient, we propose a Q-learning based resource allocation and a priority-based device clustering scheme. We prioritize the URLLC, eMBB, and mMTC devices within a cluster to meet the quality of service (QoS) requirements. Then, we formulate different NOMA constraints and incorporate them with the Q-learning algorithm. To evaluate the performance of the proposed scheme, we conduct extensive simulations under various scenarios. We can confirm that the proposed Q-learning algorithm with priority-based device clustering achieves the maximum sum-rate among all scenarios.