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

SDR-based Testbed for Real-time CQI Prediction for URLLC