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Reinforcement learning for dynamic sensing parameter control in cognitive radio systems

The interference alignment (IA) can increase the performance of the cognitive radio (CR) system by allowing multiple users to communicate simultaneously, in conjunction with the cognitive radio system. However, conventional IA-based CR researches do not meet the fundamental requirements of CR in the form of primary users participating in IA. In this paper, we allocate one SU as a sensor for sensing in the interference-based CR, and the remaining SUs are removed after being limited by the interference in the received signal space of the sensor. Then, a system is proposed in which the sensor can detect the signal of the PU in the remaining received signal space. In the proposed system, the sensor must determine the period of reporting the sensing results. For this, the reward is defined as interference ratio, loss ratio, and system load according to sensing result reply time. Using Q-learning, reinforcement learning, the sensing length and reporting period can be dynamically determined in an ever-changing environment. Simulation results show that the proposed algorithm has better performance in terms of interference ratio, loss ratio, and reward considering system load, compared to the case where the sensing length and reporting period are fixed at −12 dB.