Learning algorithm for reconfigurable antenna state selection

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.

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