Geometry-Based Stochastic Channel Model for IIoT Spatial Consistency
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Solution Overview
Problem
Existing industrial Internet of Things (IIoT) channel models lack spatial consistency, fail to accurately model dense multipath components (DMC), and do not adequately reflect the time-varying characteristics of IIoT channels.
Innovation Solution
A geometry-based stochastic channel modeling method that divides the IIoT channel impulse response into Specular Multipath Components (SMC) and DMC, using massive MIMO arrays and considering spherical waves, distance, and angle variations. The method includes parameter estimation and model validation based on Minimum Mean Square Error (MMSE) criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing IIoT channel models are used, then model simplicity is maintained, but spatial consistency and accurate modeling of dense multipath components are lost
Solution Approach 1:
The channel model is segmented into distinct propagation components: Line-of-Sight (LoS) components, Non-Line-of-Sight (NLoS) components, Specular Multipath Components (SMC), and Dense Multipath Components (DMC). Each component is modeled separately with specific geometric and statistical parameters, allowing accurate representation of complex IIoT channel characteristics while maintaining structured organization
Solution Approach 2:
Different propagation components are assigned different local characteristics: LoS components have direct geometric relationships, NLoS components have obscured paths, SMC components have deterministic reflection properties, and DMC components have stochastic distributed properties. This local differentiation enables precise modeling of spatial consistency and multipath characteristics in specific channel regions
2Reliability
If traditional channel models without DMC are used, then model simplicity is maintained, but the rich dense multipath components characteristic of IIoT scenarios with metal scatterers are not captured
Solution Approach 1:
The multipath components are segmented into SMC (deterministic specular reflections) and DMC (stochastic dense multipath). The DMC component is specifically introduced to model the rich multipath environment created by metal scatterers in IIoT factories, capturing distributed reflections that traditional models miss
Solution Approach 2:
The DMC component acts as an intermediary that bridges the gap between simple traditional models and complex physical reality. It introduces a stochastic layer that captures the essence of dense multipath from metal scatterers without requiring exhaustive modeling of every individual reflection path
3Measurement precision
If static channel models are used, then modeling complexity is reduced, but time-varying characteristics of IIoT channels are not reflected
Solution Approach 1:
The channel model incorporates time-varying characteristics through mobile transmitter and receiver positions, velocity vectors, and time-dependent geometric parameters. The model dynamically updates propagation paths, delays, angles, and powers based on changing positions, accurately reflecting the time-varying nature of IIoT wireless channels in mobile environments
Data Source
AI summary
Disclosed by the present disclosure is a geometry-based stochastic channel modeling method for an IIoT channel. The method includes the following steps: S1, setting a propagation scenario, propagation conditions, model parameters, an antenna configuration, and the like, S2, generating large-scale parameters with a spatial consistency; S3, determining a number of initial clusters, a number of specular multipath components generated in each of clusters and a number of dense multipath components generated in each of the clusters, determining a visibility of an array antenna to the clusters, generating an initial delay of the clusters, an angle of the clusters, and a power of the clusters, and generating channel coefficients between each pair of transmitter antennas and receiver antennas; S4, updating the positions of the transmitters and the positions of the receivers as well as values for the large-scale parameters according to the motion trajectories of the transmitters and the motion trajectories of the receivers; S5, applying a birth and death process of the clusters to initialize new clusters and update angles, delays and powers of surviving clusters, and generating the channel coefficients; and S6, returning to Step S4, until traversing motion trajectories of the transmitters and the motion trajectories of the receivers; calculating statistical characteristics of the channel, and verifying channel model according to actual measurement data. For the first time, the present disclosure considers 6G channel modeling requirements and dense multipath characteristics, and are verified through actual measurements, which is of great significance for the standardization of IIoT channel models.