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DEEP Reinforcement Learning Based Energy Beamforming for Powering Sensor Networks
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neuraldoi:10.1109/mlsp.2019.8918775 dblp:conf/mlsp/Ozcelikkale0SA19 fatcat:nw6iqgedyzeklpwofo5dizedem