Wireless Channel Measurements and Characterization in Industrial IoT Scenarios

TL;DR

This work targets wireless channel characterization for IIoT in factory environments using 5.5 GHz Wi‑Fi. It combines SISO and polarized MIMO measurements with a novel multi‑DMC estimation framework that extends SAGE–based SMC parameter extraction to multiple DMC processes. Key findings show abundant DMC power (30%–70%), nonuniform angular distributions, and notable polarization effects that influence delay, angular PSDs, SVs, and capacity. The results highlight the importance of incorporating DMCs for accurate IIoT channel modeling and for informing the design of robust Wi‑Fi deployments in industrial settings.

Abstract

Wireless Fidelity (Wi-Fi) communication technologies hold significant potential for realizing the Industrial Internet of Things (IIoT). In this paper, both Single-Input Single-Output (SISO) and polarized Multiple-Input Multiple-Output (MIMO) channel measurements are conducted in an IIoT scenario at the less congested Wi-Fi band, i.e., 5.5~GHz. The purpose is to investigate wireless characteristics of communications between access points and terminals mounted on automated guided vehicles as well as those surrounding manufacturing areas. For SISO channel measurements, statistical properties including the delay Power Spectral Density (PSD), path loss, shadowing fading, delay spread, excess delay, K-factor, and amplitude distribution of small-scale fading are analyzed and compared with those observed in an office scenario. For MIMO channel measurements, results show that there are multiple Dense Multipath Component (DMC) processes in the delay PSD. An estimation algorithm based on the algorithm for a single DMC process is proposed to effectively process the multi-processes data. Moreover, delay, angular, power, and polarization properties of DMCs are investigated and compared with those of specular multipath components. Furthermore, effects of DMCs on Singular Values (SVs) and channel capacities are explored. Ignoring DMCs can overestimate SVs and underestimate channel capacities.

Figures (17)

• Figure 1: Time domain 32$\times$64 MIMO channel sounder.
• Figure 2: Measurement positions of (a) SISO and (b) MIMO channel measurements in IIoT scenarios.
• Figure 3: Comparisons of (a) initial and (b) iterative estimation results for one DMC process as well as (c) initial and (d) iterative estimation results for three DMC processes.
• Figure 4: Measured delay PSDs in IIoT scenarios at (a) position Rx71 of Case 1 and (b) position Rx281 of Case 2.
• Figure 5: PL of measurement, CI model, and FI model results for Case 1, Case 2, and office scenarios in (a) LOS scenarios and (b) NLOS scenarios.