  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.
