Method for selecting state of a reconfigurable antenna in a communication system via machine learning
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United States of America Patent
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Mar 20, 2018
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Sep 28, 2015
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Sep 8, 2011
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Abstract
A method for selecting the state of a reconfigurable antenna installed at either the receiver or transmitter of a communication system is provided. The proposed method uses online learning algorithm based on the theory of multi-armed bandit to perform antenna state selection. The selection technique utilizes the Post-Processing Signal-to-Noise Ratio (PPSNR) as a reward metric and maximizes the long-term average reward over time. The performance of the learning based selection technique is empirically evaluated using wireless channel data. The data is collected in an indoor environment using a 2×2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. The learning based selection technique shows performance improvements in terms of average PPSNR and regret over conventional heuristic policies.
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