Image Method Based 6G Channel Modeling for IIoT and Mobility Scenarios

Abstract

Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.