Priority-Based Joint Resource Allocation With Deep Q-Learning for Heterogeneous NOMA Systems

Abstract

For heterogeneous demands in fifth-generation (5G) new radio (NR), a massive machine type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable and low-latency communication (URLLC) services have been introduced. To ensure these quality-of-service (QoS) requirements, non-orthogonal multiple access (NOMA) has been introduced in which multiple devices can be served from the same frequency by manipulating the power domain and successive interference cancellation (SIC) technique. To maximize the efficiency of NOMA systems, an optimal resource allocation, such as power allocation and channel assignment, is a key issue that needs to be solved. Although many researchers have proposed multiple solutions, there have been no studies addressing the 5G QoS requirements and three services that coexist in the same network. In this paper, we formulate an optimal power allocation scheme under Karush–Kuhn–Tucker (KKT) optimality conditions incorporating different NOMA constraints to maximize the channel sum-rate and system fairness. We then propose a priority-based channel assignment with a deep $Q$ -learning algorithm to maintain the 5G QoS requirements and increase the network performance. Finally, We conduct extensive simulations with respect to different system parameters and can confirm that the proposed scheme performs better than other existing schemes.

Keywords

• Deep Q-learning
• Internet of Things
• joint resource allocation
• non-orthogonal multiple access (NOMA)