An overview of deep reinforcement learning for spectrum sensing in cognitive radio networks

Deep reinforcement learning has recorded remarkable performance in diverse application areas of artificial intelligence: pattern recognition, robotics, object segmentation, recommendation-system, and gaming. In recent times, the applicability of deep learning to telecommunication technology is gradually attracting a lot of attention, especially in spectrum sensing, a core component in cognitive radio. The traditional approaches to spectrum sensing are heavily prone to noise uncertainty and often rely on either complete or partial prior knowledge of the primary users. An alternative method that can curb the aforementioned problem is deep reinforcement learning, which integrates several layers of neural networks for extracting and learning features automatically from a given data. Hence, we survey and propose a theoretical hypothetic model formulation of deep reinforcement learning as an effective method for creating a cooperative spectrum sensing model that can overcome the limitations of traditional spectrum sensing methods, which are often prone to low sensing precision. Also, the study provides an overview of past, current, and future advances in cognitive radio networks. The discussion herein will be of interest to a wide range of audiences in telecommunication and artificial intelligence.