Deep-Q Reinforcement Learning for Fairness in Multiple-Access Cognitive Radio Networks
Zain Ali, Zouheir Rezki, Hamid R. Sadjadpour · 2022 IEEE Wireless Communications and Networking Conference (WCNC) · 2022
This work presents a deep-Q reinforcement learning (DQ-RL) framework to achieve fairness in multi-access cognitive radio (CR) systems. The proposed framework provides fast solution and is robust to channel dynamics. Further, to remove the computational overhead and the burden to feedback thousands of weights from the secondary receiver (SR), we propose a solution where the process of learning is carried out at the secondary transmitters (STs). The simulations show that by using the proposed technique, a good level of fairness is achievable with an outage probability of the primary system less than 0.04. We also provide the comparison of the proposed technique with a brute-forcing optimization method, and show the fairness gain of the proposed framework compared to the rate maximization model.