Qureshi, Muhammad AnjumTekin, Cem2021-03-042021-03-04202097817281641370743-166Xhttp://hdl.handle.net/11693/75749Date of Conference: 6-9 July 2020Conference Name: 38th IEEE Conference on Computer Communications, INFOCOM 2020We consider the problem of dynamic rate selection in a cognitive radio network (CRN) over the millimeter wave (mmWave) spectrum. Specifically, we focus on the scenario when the transmit power is time varying as motivated by the following applications: i) an energy harvesting CRN, in which the system solely relies on the harvested energy source, and ii) an underlay CRN, in which a secondary user (SU) restricts its transmission power based on a dynamically changing interference temperature limit (ITL) such that the primary user (PU) remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information (CSI) is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling (TS) based Bayesian method. Our method utilizes the unimodality and monotonicity of the throughput with respect to rates and transmit powers and achieves logarithmic in time regret with a leading term that is independent of the number of available rates. Our regret bound holds for any sequence of transmits powers and captures the dependence of the regret on the arrival pattern. We also show via simulations that the performance of the proposed algorithm is superior than the stateof-the-art algorithms, especially when the arrivals are favorable.EnglishCognitive radio networksmmWaveDynamic rate selectionThompson samplingContextual unimodal banditsConference Paper10.1109/INFOCOM41043.2020.915553097817281641202641-9874