A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond
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Abstract (arXiv)
In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jan 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 4; 2026: 1.
6 papers.
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- 2025A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modelingcited by 0
- 2025RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimizationcited by 0
- 2026Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learningcited by 0
- 2026Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning26 Sep 2026
- 2025RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization17 Sep 2025
- 2025A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction13 Jul 2025
- 2025A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling3 Mar 2025
- 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments3 Mar 2025
- 2022Simulation of machine learning-based 6G systems in virtual worlds15 Apr 2022