The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 20, 2018

Filed:

Sep. 28, 2015
Applicant:

Drexel University, Philadelphia, PA (US);

Inventors:

Nikhil Gulati, Philadelphia, PA (US);

David Gonzalez, Annandale, NJ (US);

Kapil R. Dandekar, Philadelphia, PA (US);

Assignee:

Drexel University, Philadelphia, PA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04B 1/44 (2006.01); H04W 72/08 (2009.01); H01Q 1/00 (2006.01); H01Q 13/20 (2006.01); H04W 24/02 (2009.01); H04B 7/0417 (2017.01); H04B 7/06 (2006.01);
U.S. Cl.
CPC ...
H04W 72/082 (2013.01); H01Q 1/007 (2013.01); H01Q 13/20 (2013.01); H04B 1/44 (2013.01); H04B 7/0417 (2013.01); H04B 7/0626 (2013.01); H04W 24/02 (2013.01); H04W 72/085 (2013.01);
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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