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Online Power Adaption for Energy Efficiency in Cognitive Radio Networks

Energy efficiency is an important issue for cognitive radio networks, in which each cognitive user must acknowledge experienced environment, besides maintaining communication. Existing works on energy efficiency have been focused on power control in traditional networks (e.g. CDMA network), supposing that the network environment is static. However, there has not been works on energy efficiency under dynamic environment. This paper addresses that energy efficiency is defined by a long-term valuation function, which describes that each user selects the optimal policy to maximize their valuation, in which each user adjusts its power according to dynamic propagation environment and competition caused by other selfish users. The proposed algorithm, online power adaption (OPA), contains three parts: dynamic environment model, dynamic learning, and policy selection. Compared with myopic adaption and average adaption, the simulation results demonstrated that the OPA's performance is better than the other algorithms.