  Ultra-reliable Low-Latency Communication (URLLC) is a key feature of 5G
systems. The quality of service (QoS) requirements imposed by URLLC are less
than 10ms delay and less than $10^{-5}$ packet loss rate (PLR). To satisfy such
strict requirements with minimal channel resource consumption, the devices need
to accurately predict the channel quality and select Modulation and Coding
Scheme (MCS) for URLLC in a proper way.
  This paper presents a novel real-time channel prediction system based on
Software-Defined Radio that uses a neural network. The paper also describes and
shares an open channel measurement dataset that can be used to compare various
channel prediction approaches in different mobility scenarios in future
research on URLLC
