Performance Comparison of Learning Techniques for Intelligent Channel Assignment in Cognitive Wireless Sensor Networks

With the increasing number of devices sharing the 2.4 GHz ISM band, coexistence problem becomes one of the major issues experienced by Wireless Sensor Networks (WSN). Cognitive Wireless Sensor Networks (CWSNs) has been proposed in order to achieve reliable and efficient communication via spectrum awareness and intelligent adaption. The learning and decision making technique is one of the core competences of such system. In this work, there machine-learning techniques under the umbrella of Reinforcement Learning (RL) including GPOMDP, Episodic-Reinforcement, and True Policy Gradient are implemented for our proposed learning and decision making engine of CWSN. Simulation model has been developed and used for the investigation and the results are obtained for performance comparison in terms of prediction accuracy and WSN system performance. From this study, True Policy Gradient offers better prediction accuracy in comparison with the other two techniques. As results, CWSN implementing True Policy Gradient offers lowest packet delay under interference environment.