Talk Title: Channel parameter estimation algorithms for exploring refined characteristics of wave propagation

With deeper research on 6G wireless systems, complex-scenario communications need better wireless channel feature extraction. Creating multi-dimensional channel resources to back future “comms-sensing-computing” networks (fast, reliable, resilient) is key to channel research. Recently, massive antenna arrays and RIS have shown limits for the conventional sparsity assumption applied for multipath-based channel modeling. Studying how propagation traits interact with the environment deterministically is a new way to model channels accurately. Based on our laboratory’s in-depth research in recent years, this report summarizes some parametric model innovations for exploring refined characteristics of channels. It emphasizes the method of resolving the overall environment through individual scatterers, highlights the correlation between channel responses and 3D-space plus time, and performs posterior optimization on model selection and combination. We focus on application scenarios with large bandwidth, large arrays, and high time-variation. Using the basic concepts of spatial segmentation, geometric representation of complex wavefronts, and orthogonal segmentation of static and dynamic components, we established multiple estimation algorithms for topological and morphological perception parameters. It is expected to expand the exploration space for establishing novel wireless channel models for future applications.