SDR-based Testbed for Real-time CQI Prediction for URLLC
This work addresses the challenge of efficient channel resource consumption for URLLC in 5G, but it appears incremental as it applies a neural network to a known bottleneck in channel prediction.
The paper tackles the problem of accurately predicting channel quality for Ultra-reliable Low-Latency Communication (URLLC) in 5G systems to meet strict QoS requirements like less than 10ms delay and less than 10^-5 packet loss rate, and presents a real-time channel prediction system using a neural network based on Software-Defined Radio, along with an open dataset for future comparisons.
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