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.
@article{arxiv.1911.09292,
title = {Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT},
author = {Vanlin Sathya and Adam Dziedzic and Monisha Ghosh and Sanjay Krishnan},
journal= {arXiv preprint arXiv:1911.09292},
year = {2019}
}
Comments
Published at International Conference on Computing, Networking and Communications (ICNC 2020)