3GPP 3D Channel Model Research Articles

Multi-Layer Multi-User MIMO With Cylindrical Arrays Under 3GPP 3D Channel Model for B5G/6G Networks

Multi-Layer Multi-User MIMO With Cylindrical Arrays Under 3GPP 3D Channel Model for B5G/6G Networks

Sparse Channel Estimation for Spatial Non-Stationary Massive MIMO Channels

In massive MIMO systems, the channel shows a spatial non-stationarity that has a significant impact on the design of sparse channel estimation scheme based on compressive sensing, due to the large size of antenna array. In this letter, the block matching pursuit (BMP)-based two-stage sparse channel estimation scheme is proposed for spatial non-stationary (SNS) channels. In this scheme, the BMP is introduced to search for the support blocks and the threshold-based denoising method is utilized to refine the supports of non-zero elements. In order to evaluate our scheme, we provide a simple SNS channel model modified from the 3GPP 3D channel model to capture the delay-domain special sparse characteristic. Simulation results demonstrate that the proposed scheme can recover channels effectively and achieve better performance than comparison methods.

A Spatial Consistency Model for Geometry-Based Stochastic Channels

Antennas with a massive amount of elements at one end are among 5G mobile communication key technologies for which spectral efficiency is enhanced by serving many users in parallel over tailored minimally interfering beams. This requires channel models that characterize the propagation environment in both azimuth and elevation. Additionally, the channel model has to capture spatial correlation effects among closely located positions, knowing that the propagation characteristics change gradually over the network area. In order to simulate mobile users or advanced beamforming strategies based on user location or angular information, it is crucial that spatial consistency is included in the applied channel models. This paper introduces a novel model for spatial consistency that is applicable to all prevalent geometry-based stochastic channel models. We provide a detailed explanation of the model and analyze its statistical properties and show its behavior when applied to the 3GPP 3D channel model as an example. To validate our model, we perform extensive ray-tracing simulations and show that our model is in a very good agreement with the statistical channel properties from ray-tracing. Following hypothesis testing over obtained ray-tracing statistics, we are able to parametrize our model for various 3GPP scenarios under LOS and NLOS propagation conditions. Finally, complementary aspects such as simulation complexity are discussed and a guideline on model implementation is provided.