Reinforcement Learning for Resource Allocation in Cognitive Radio Networks
Andres Kwasinski, Wenbo Wang, Fatemeh Shah Mohammadi · 2019
This chapter discusses the use of machine learning to perform distributed resource allocation in cognitive radio (CR) networks. There are many reinforcement learning techniques; one of the most common is Q-learning. The chapter explains the use of Q-learning for cross-layer resource allocations and describes resource allocation based on the deep Q-learning technique. It shows how different CRs can cooperate during the learning process. The chapter illustrates the performance of the table-based Q-learning algorithm for cross-layer resource allocation and the performance impact when implementing cooperative learning. The figures compare the results from simulations of three different systems: a system performing joint cross-layer CR adaptation, called individual learning; a system called docitive that also performs joint cross-layer CR adaptation but considers a secondary user joining the network that learns through the cross-layer docitive approach; and a system identified as physical layer only.