Interference Avoidance Scheme Based on Reinforcement Learning
Fuchang Li, Wei Shao, Qisen Zhou, Jiaxing Zhao · 2020
This paper proposes an interference avoidance scheme based on reinforcement learning for the communication stations exposed to interference. Firstly, we design the signal to interference noise ratio-mean opinion score (SINR-MOS) as the reward function to obtain optimal channel handoff strategy through reinforcement learning. Then, two specific methods of interference avoidance are proposed by applying Q-learning algorithm, which enables an automatic avoidance by switching to a new idle channel when the station is interfered. Finally, it is proved by computer simulation experiments that the methods have a low miss detection rate and false alarm rate, showing good interference avoidance performance.