Spectrum Situation Awareness Based on Time-Series Depth Networks for LTE-R Communication System

The Long Term Evolution for Railway (
LTE-R
) communication system is providing a reliable data link for High-Speed Railway (
HSR
) communication. However, when the train passes through different railway environments, the channel capacity of the base station and the number of users are always in highly dynamic changes. Therefore, accurate predicting the changing law of wireless spectrum resources can make more efficient use of wireless spectrum resources. The purpose of this paper is to use the Long Short-Term Memory network (
$LST\!M$
) to predict the channel occupancy changes of wireless spectrum resources. Under the premise of ensuring the safe and reliable service for primary users (
PU
), it provides a feasible method for the secondary user’s (
SU
) opportunistic access to the authorized channels, thereby improving the
LTE-R
system Utilization rate of spectrum resources. Based on the “occupied/idle” status of the authorized channel at the previous
$n$
historical moments, we infer the status of the authorized channel at the current moment, build the spectrum situation of the authorized channels, and guide the SU to conduct Dynamic opportunistic Spectrum Access (
DSA
) to the authorized channels. The simulation results show that when SU uses the channel situation constructed by the
$LST\!M$
network to access the authorized channels, it has fewer handovers and lower collision rates, and can obtain higher throughput.