Learning algorithm for reconfigurable antenna state selection

Author

Gulati, Nikhil ; Gonzalez, David ; Dandekar, Kapil R.

Author_Institution

Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA

fYear

2012

Firstpage

31

Lastpage

34

Abstract

In this paper, we propose an online learning algorithm for selecting the state of a reconfigurable antenna. We formulate the antenna state selection as a multiarmed bandit problem and present a selection technique, implemented for a 2 × 2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. We quantify the performance of our selection technique using a software defined radio testbed and present results for a wireless network in a typical indoor environment.

Keywords

MIMO communication; OFDM modulation; directive antennas; electrical engineering computing; indoor environment; learning (artificial intelligence); metamaterial antennas; software radio; telecommunication computing; MIMO OFDM system; directional metamaterial reconfigurable leaky wave antenna; indoor environment; multiarmed bandit problem; online learning algorithm; reconfigurable antenna state selection technique; software defined radio testbed; wireless network; MIMO; Receiving antennas; Transmitting antennas; Wireless communication; Learning algorithms; MIMO; OFDM; bandit problem; reconfigurable antennas;

fLanguage

English

Publisher

ieee

Conference_Titel

Radio and Wireless Symposium (RWS), 2012 IEEE

Conference_Location

Santa Clara, CA

ISSN

2164-2958

Print_ISBN

978-1-4577-1153-4

Type

conf

DOI

10.1109/RWS.2012.6175375

Filename

6175375