Energy efficient dynamic bid learning model for future wireless network

In this paper, an energy efficient learning model for spectrum auction based on dynamic spectrum auction process is proposed. The proposed learning model is based on artificial intelligence. This paper examines and establishes the need for the users to learn their bid price based on information about the previous bids of the other users in the system. The paper shows that using Q reinforcement learning to learn about the bids of the users during the auction process helps to reduce the amount of energy consumed per file sent for the learning users. The paper went further to modify the traditional Q reinforcement learning process and combined it with Bayesian learning because of the deficiencies associated with Q reinforcement learning. This helps the exploration process to converge faster thereby, further reducing the energy consumption by the system.