Online dynamic resource allocation in interference temperature constrained cognitive radio network using reinforcement learning

This paper focuses on the allocation of spectrum and power in the cognitive radio devices in the wireless network. This new approach suggests a learning procedure of allocating spectrum to the unlicensed users dynamically. This article also highlights learning procedure of allocating power for increasing the throughput ratio, considering the interference issues into account. The learning automata model is used to allocate spectrum and power to transceivers.