ENERGY EFFICIENT BID LEARNING PROCESS IN AN AUCTION BASED COGNITIVE RADIO NETWORK
Publication Date : 01/08/2016
This paper proposes a learning based auction model for cognitive radio network using the concept of Bayesian and Q-learning. A learning process is introduced to aid energy efficiency in an auction based cognitive system. By using Q-learning to learn the bid price, this paper showed that for the learning users, the amount of energy consumed per file sent can be reduced when compared to the non-learning users. Furthermore, to overcome the deficiencies of tra-ditional Q-learning we bias the exploration process with Bayesian learning. This helps the exploration process to converge faster, thereby further reducing the energy consumption by the learning users in the system and the system delay.
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