Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning
AbstractThe use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception. View Full-Text
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Almeida, N.C.; Fernandes, M.A.; Neto, A.D. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning. Sensors 2015, 15, 6668-6687.
Almeida NC, Fernandes MA, Neto AD. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning. Sensors. 2015; 15(3):6668-6687.Chicago/Turabian Style
Almeida, Náthalee C.; Fernandes, Marcelo A.; Neto, Adrião D. 2015. "Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning." Sensors 15, no. 3: 6668-6687.