Upper Confidence Bound learning approach for real HF measurements

New strategies based on cognitive radio are being discussed to make a more efficient use of the HF band. Multiple users transmit in this band with a worldwide coverage but having multiple collisions with other HF stations. The use of the Upper Confidence Bound (UCB) algorithm is proposed in this work to provide them with a dynamic spectrum access mitigating mutual interference. Based on reinforcement learning, it is used to select the best channel of a wideband HF transceiver in terms of availability. The feasibility of this proposal is demonstrated with real measurements of amateur contests in the HF band. To the best of the authors´ knowledge, this is one of the few works on learning with real HF measurements.