Learning State Selection for Reconfigurable Antennas: A Multi-Armed Bandit Approach

Reconfigurable antennas are capable of dynamically re-shaping their radiation patterns in response to the needs of a wireless link or a network. In order to utilize the benefits of reconfigurable antennas, selecting an optimal antenna state for communication is essential and depends on the availability of full channel state information for all the available antenna states. We consider the problem of reconfigurable antenna state selection in a single user MIMO system. We first formulate the state selection as a multi-armed bandit problem that aims to optimize arbitrary link quality metrics. We then show that by using online learning under a multi-armed bandit framework, a sequential decision policy can be employed to learn optimal antenna states without instantaneous full CSI and without a priori knowledge of wireless channel statistics. Our objective is to devise an adaptive state selection technique when the channels corresponding to all the states are not directly observable and compare our results against the case of a known model or genie with full information. We evaluate the performance of the proposed antenna state selection technique by identifying key link quality metrics and using measured channels in a 2 X 2 MIMO OFDM system. We show that the proposed technique maximizes long term link performance with reduced channel training frequency.