Reinforcement learning based distributed multiagent sensing policy for cognitive radio networks

@article{Lundn2011ReinforcementLB,
  title={Reinforcement learning based distributed multiagent sensing policy for cognitive radio networks},
  author={Jarmo Lund{\'e}n and Visa Koivunen and Sanjeev R. Kulkarni and H. Vincent Poor},
  journal={2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)},
  year={2011},
  pages={642-646}
}
In this paper a distributed multiagent, multiband reinforcement learning based sensing policy for cognitive radio ad hoc networks is proposed. The proposed sensing policy employs secondary user (SU) collaboration through local interactions. The goal is to maximize the amount of available spectrum found for secondary use given a desired diversity order, i.e. a desired number of SUs sensing simultaneously each frequency band. The SUs in the cognitive radio network make local decisions based on… CONTINUE READING
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