Machine Learning based detection of multiple Wi-Fi BSSs for LTE-U CSAT

TL;DR

This paper introduces a machine learning method to accurately detect the number of Wi-Fi BSSs operating on a channel in real-time using energy measurements during LTE-U OFF periods, without decoding Wi-Fi packets.

Contribution

The paper presents a novel ML-based approach for real-time Wi-Fi BSS detection using energy data, validated through real-world experiments, achieving near-perfect accuracy.

Findings

Achieved close to 100% detection accuracy.

Identified distinct energy patterns for different numbers of Wi-Fi BSSs.

Outperformed traditional auto-correlation and energy detection methods.

Abstract

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.