Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications
This addresses the complexity and signaling overhead problem in V2X communications for autonomous vehicles and 6G networks, representing an incremental improvement over existing low-rank methods.
The paper tackles the challenge of MIMO channel estimation in high-mobility 6G V2X communications by proposing a deep learning method that infers channel eigenmodes from a single least-squares estimate without requiring vehicle position information, achieving comparable mean squared error performance to position-based algebraic low-rank methods and demonstrating transferability across different urban scenarios.
In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time domain (i.e., directions or arrival/departure and delays). Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signalling overhead. Here we design a DL-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single LS channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable Mean Squared Error (MSE) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different space-time channel features, providing comparable MSE performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.