  In this paper, we present Sense-Bandits, an AI-based framework for
distributed adaptation of the sensing thresholds (STs) over shared spectrum.
This framework specifically targets the coexistence of heterogenous
technologies, e.g., Wi-Fi, 4G Licensed-Assisted Access (LAA), and 5G New Radio
Unlicensed (NR-U), over unlicensed channels. To access the channel, a device
compares the measured power with a predefined ST value and accordingly decides
if the channel is idle or not. Improper setting of the ST values creates
asymmetric sensing floors, resulting in collisions due to hidden terminals
and/or reduction in the spatial reuse due to exposed terminals. Optimal ST
setting is challenging because it requires global knowledge of mobility,
traffic loads, and channel access behavior of all contending devices. Sense-
Bandits tackles this problem by employing a clustering-based multi-armed bandit
(MAB) algorithm, which adapts its learning behavior based on network dynamics.
Clustering allows the algorithm to track network changes in real-time, ensuring
fast learning of the best ST values by classifying the state and dynamics of
coexisting networks. We develop a C++-based network simulator that implements
Sense-Bandits and we apply it to evaluate the coexistence of Wi-Fi and 5G NR-U
systems over the unlicensed 5 GHz U NII bands. Our simulation results indicate
that ST-adaptive devices employing Sense-Bandits do not harm neighboring
devices that adopt a fixed ST value.
