Reinforcement learning-based cooperative sensing in cognitive radio networks for primary user detection

Cognitive radio networks achieve a better utilisation of spectrum through spectrum sharing. Due to interference, power levels and hidden terminal problem, it becomes challenging to detect the presence of primary users accurately and without this, spectrum sharing cannot be optimised. Thus, detection of primary users has become an important research problem in cognitive radio network. Existing solutions have low accuracy when effect of multipath fading and shadowing are considered. Reinforcement-based learning solutions are able to learn the environment dynamically and able to achieve higher accuracy in detection of primary users. However, the computational complexity and latency is higher in the previous solutions on application of reinforcement learning to spectrum sensing. In this work, reinforcement learning model is proposed to detect the presence of primary user. This approach has higher accuracy due to reliance on multi-objective functions and reduced computational complexity.