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Deep Learning Opportunities for Resource Management in Cognitive Radio Networks

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Abstract

This chapter discusses the use of machine and deep learning in spectrum and resource optimisation for the cognitive radio networks. Indeed, modern cognitive radio networks are being developed to employ artificial intelligence strategies for dynamic spectrum sensing and decision-making processes in order to achieve optimum and accurate decisions. Contrary to traditional wireless communications, learning goal-directed behaviour in dynamic and distributed environments like the cognitive radio networks still pose major challenges for the machine learning strategies being developed. The key difficulty lies in insufficient exploration of the state space, which results in agents being unable to learn robust policies. However, through the use of deep architecture such as deep learning and deep reinforcement learning, agents can explore new behaviour and could eventually help the agent solve tasks posed by the environment. The chapter explores and investigates deep architecture being applied in addressing spectrum management problems in the cognitive radio networks. The most integral part of this chapter is the discussion on how these deep architecture are orchestrated or tailored to solve different problems in the cognitive radio networks.

Keywords

Cognitive radio networks Resource allocation Artificial intelligence Deep learning Machine learning Deep reinforcement learning Deep architecture 

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Authors and Affiliations

  1. 1.University of PretoriaPretoriaSouth Africa

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