Experimental Analysis of 5G NR for Indoor Industrial Environments

Abstract

Private 5G networks for industrial users are emerging as one of the leading advanced 5G use cases. This timely work presents a comprehensive experimental analysis of a private 5G network conducted in sparse and dense industrial environments at sub-6 GHz. Measured results of the over-the-air error vector magnitude (EVM) are provided, considering signal-to-noise ratio (SNR) for different 5G new radio modulation and coding schemes (MCSs), bandwidths (BWs) and numerologies (subcarrier spacings) using omnidirectional or directional antenna configurations at the transmitter (TX) and the receiver (RX). Channel sounding measurements are also conducted to characterise the channels in terms of root mean square (RMS) delay spread. The measurement results show that channels in the dense industrial environment have greater RMS delay spreads than in the sparse industrial environment due to strong reflected or scattered multipath components with significant delays. This results in higher EVMs and bit error rates (BERs), i.e., as the RMS delay spread increases, a higher SNR is required to meet the EVM limits. It is also observed that using directional antennas at the TX and RX in both environments reduces the RMS delay spread and hence the inter-symbol interference and the EVM. This allows higher MCS modes (e.g., 64 QAM and 256 QAM) to be used for reliable data transmission, significantly improving the bandwidth efficiency and reducing the latency. When evaluating system performance for different BWs and numerologies, using a lower BW and numerology provides a better system performance (lower EVMs and BERs), especially in dense industrial environments.

Keywords

• 5G NR
• industrial environment
• Industrial Internet of Things (IIOT)
• measured EVM
• numerology
• smart factory