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

TL;DR

This paper introduces a real-time SDR-based neural network system for predicting channel quality in URLLC, aiming to meet strict latency and reliability requirements in 5G networks.

Contribution

It presents a novel SDR-based testbed utilizing neural networks for real-time channel quality prediction in URLLC and provides an open dataset for future research.

Findings

Achieved accurate real-time channel quality prediction

Demonstrated effectiveness in various mobility scenarios

Shared a valuable open dataset for the community

Abstract

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