Resource Allocation for Cognitive Radio Inspired Non-Orthogonal Multiple Access Networks: A Quantum Soft Actor-Critic Method

With the growth of the communication industry, the demand for spectrum resources has been increasing steadily. However, the spectrum resources are limited and the utilization rate is low. In this case, Cognitive Radio (CR) technologies and Non-Orthogonal Multiple Access (NOMA) technology are proposed to improve the number of user access and spectrum resource utilization. In this paper, we consider a CR-inspired NOMA network and propose a power allocation problem based on a time-varying system. Due to the dynamic nature of CR, traditional methods are often faced with problems such as low training efficiency and high network volatility. To address the problem, we propose a novel Quantum Reinforcement Learning (QRL) algorithm with quantum world model, interacting with the environment in the quantum way. Specifically, we construct a quantum soft actor-critic algorithm using variable quantum circuit (VQC), and the experience data is encoded into a quantum sum tree and transformed into quantum world model. Simulation results show the superior performance of the proposed framework.