Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT
This solves a specific problem for LTE-U base-stations in dynamically adjusting duty cycles based on Wi-Fi interference, though it appears incremental as it builds on prior detection methods.
The paper tackled the challenge of detecting the number of co-channel Wi-Fi BSSs in real-time without decoding packets, using a novel ML approach based on energy values during LTE-U OFF periods, achieving close to 100% accuracy compared to existing methods.
According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi BSSs that are operating on the channel in real-time, without decoding the Wi-Fi packets, still remains a challenge. In this paper, we present a novel machine learning (ML) approach that solves the problem by using energy values observed during LTE-U OFF duration. Observing the energy values (at LTE-U BS OFF time) is a much simpler operation than decoding the entire Wi-Fi packets. In this work, we implement and validate the proposed ML based approach in real-time experiments, and demonstrate that there are two distinct patterns between one and two Wi-Fi APs. This approach delivers an accuracy close to 100% compared to auto-correlation (AC) and energy detection (ED) approaches.