Sense-Bandits: AI-based Adaptation of Sensing Thresholds for Heterogeneous-technology Coexistence Over Unlicensed Bands
Mohammed Hirzallah, Marwan Krunz

TL;DR
Sense-Bandits is an AI-driven framework that dynamically adapts sensing thresholds for heterogeneous wireless technologies sharing unlicensed spectrum, improving coexistence and spectrum efficiency.
Contribution
It introduces a clustering-based multi-armed bandit algorithm for real-time adaptive sensing threshold setting in heterogeneous wireless networks.
Findings
Adaptive STs do not harm fixed-ST devices.
Sense-Bandits improves coexistence in Wi-Fi and 5G NR-U.
Real-time network change tracking enhances learning speed.
Abstract
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…
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