# Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

arXiv: https://arxiv.org/abs/2108.13831 · HTML: https://mlsanity.com/paper/2108.13831/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://mlsanity.com/paper/1912.12265/) (2019)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [Automatic Differentiation for Complex Valued SVD](https://mlsanity.com/paper/1909.02659/) (2019)
- [Channel Estimation for 6G V2X HybridSystems using Multi-Vehicular Learning](https://mlsanity.com/paper/2105.09689/) (2021)
- [Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning](https://mlsanity.com/paper/2107.12098/) (2021)

## Abstract (arXiv)

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.