  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.
