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Sensors 2015, 15(3), 6668-6687; doi:10.3390/s150306668

Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

1
UFERSA—Federal Rural University of the Semi-Árido, Pau dos Ferros 59900-000, Brazil
2
DCA-CT-UFRN, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 31 October 2014 / Revised: 14 February 2015 / Accepted: 4 March 2015 / Published: 19 March 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1504 KB, uploaded 19 March 2015]   |  

Abstract

The 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
Keywords: beamforming; power control; sensor arrays; Q-learning beamforming; power control; sensor arrays; Q-learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Almeida, N.C.; Fernandes, M.A.; Neto, A.D. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning. Sensors 2015, 15, 6668-6687.

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