Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

Sensors

Náthalee C. Almeida²  Marcelo A.C. Fernandes¹  Adrião D.D. Neto¹ 

[1] DCA-CT-UFRN, Federal University of Rio Grande do Norte, Natal 59072-970, Brazil; E-Mails:;UFERSA—Federal Rural University of the Semi-Árido, Pau dos Ferros 59900-000, Brazil

关键词: beamforming;    power control;    sensor arrays;    Q-learning;   

DOI  :  10.3390/s150306668

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.