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Base Station Switching Using Transfer Actor- Critic Learning Algorithm for Energy Saving In Heterogeneous Networks

Ramya.R, Pratheba.M

The explosive popularity of smart phones and tablets has ignited a surging traffic load demand for radio access and there has been massive energy consumption. .The reason behind is this largely due to that the present BS deployment on the basis of peak traffic loads and generally stays active irrespective of the heavily dynamic traffic load variations. There is a need to reduce energy consumption at BS. In this project using TACT (Transfer Actor-Critic Learning Algorithm), developed base station switching operations to match up with traffic load variations. This scheme is designed to minimize the energy consumption of Radio Access Networks (RAN). Ultimate aim is to reduce the energy consumption with traffic load variations in radio access networks. Markov decision process and testing results are presented in this project