Learning-based WiFi Traffic Load Estimation in NR-U Systems

TL;DR

This paper introduces a machine learning approach using neural networks to accurately estimate WiFi traffic loads in NR-U systems, ensuring fair coexistence in unlicensed spectrum bands.

Contribution

It presents an unsupervised neural network method with online training and adaptive optimization for precise WiFi user estimation in unlicensed bands.

Findings

Lower complexity than Kalman Filter methods

More stable and accurate WiFi load estimation

Effective in dynamic unlicensed spectrum environments

Abstract

The unlicensed spectrum has been utilized to make up the shortage on frequency spectrum in new radio (NR) systems. To fully exploit the advantages brought by the unlicensed bands, one of the key issues is to guarantee the fair coexistence with WiFi systems. To reach this goal, timely and accurate estimation on the WiFi traffic loads is an important prerequisite. In this paper, a machine learning (ML) based method is proposed to detect the number of WiFi users on the unlicensed bands. An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users. Moreover, NN is trained online and the related parameters and learning rate of NN are jointly optimized to estimate the number of WiFi users adaptively with high accuracy. Simulation results demonstrate that compared with the conventional Kalman Filter based detection mechanism, the proposed approach has lower complexity and can achieve a more stable and accurate estimation.