Title: Spectrum Access In Cognitive Radio Using A Two Stage Reinforcement Learning Approach,
Abstract: With the advent of the 5th generation of wireless standards and an increasing
demand for higher throughput, methods to improve the spectral efficiency of
wireless systems have become very important. In the context of cognitive radio,
a substantial increase in throughput is possible if the secondary user can make
smart decisions regarding which channel to sense and when or how often to
sense. Here, we propose an algorithm to not only select a channel for data
transmission but also to predict how long the channel will remain unoccupied so
that the time spent on channel sensing can be minimized. Our algorithm learns
in two stages - a reinforcement learning approach for channel selection and a
Bayesian approach to determine the optimal duration for which sensing can be
skipped. Comparisons with other learning methods are provided through extensive
simulations. We show that the number of sensing is minimized with negligible
increase in primary interference; this implies that lesser energy is spent by
the secondary user in sensing and also higher throughput is achieved by saving
on sensing.