Optimal probabilistic policy for dynamic resource activation using markov decision process in green wireless networks

With increasing awareness toward protecting our environment, this paper intends to reduce the CO2 emission of a wireless cellular network by reducing the power consumption of its base station. We propose to reduce power consumption by dynamically activating and deactivating the modular resources at the base station depending on the instantaneous network traffic. In order to achieve the objective, we develop a discrete time Markov Decision Process (DTMDP) to capture the dynamics of the system. In the DTMDP, the action to be taken at each decision epoch is to activate a new resource module, to deactivate a currently active resource module, or to stay the same. We further develop a linear programming approach to solve the DTMDP for optimal probabilistic decision policy. Evaluation results show that the optimal probabilistic policy for resource activation can reduce power consumption for more 50 percent under various traffic load conditions, without compromising network service quality which is measured in terms of user blocking probability.