Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25217 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914513301798912 |
|---|---|
| author | Wu, Zhengyuan Zhao, Junhui Zhang, Qingmiao Zhang, Ming |
| author_facet | Wu, Zhengyuan Zhao, Junhui Zhang, Qingmiao Zhang, Ming |
| contents | The development of intelligent and diversified ser vices in urban rail transit (URT) has resulted in an increasing de mand for high-rate communication between vehicles and ground equipment. However, existing URT communication systems strug gle to handle the massive data exchange required for vehicle-to ground (V2G) communication. To address this issue, we propose a distributed dual-polarized MIMO architecture suitable for URT tunnel scenarios. Specifically, the channel model is based on spatial three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM), which takes into account the geometric distribution of URT tunnels and the cross-polarization effects between dual-polarized antennas. For dual-polarized MIMO systems, the polarized-aware sparse channel estimation (PASCE) method is proposed for effective channel estimation. Additionally, we derive closed-form expressions for the MMSE and MR precoding schemes. The polarized-aware dynamic interference cancellation (PADIC) algorithm is developed to eliminate in terference between different polarization modes and multiple users. The simulation results demonstrate that the proposed dual-polarized precoding algorithm can withstand high cross polarization correlation (XPC) and improve the efficiency of V2G communication to achieve high rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25217 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dual-Polarized Massive MIMO Based on Precoding for Vehicle-To-Ground Communication in Urban Rail Transit Wu, Zhengyuan Zhao, Junhui Zhang, Qingmiao Zhang, Ming Systems and Control The development of intelligent and diversified ser vices in urban rail transit (URT) has resulted in an increasing de mand for high-rate communication between vehicles and ground equipment. However, existing URT communication systems strug gle to handle the massive data exchange required for vehicle-to ground (V2G) communication. To address this issue, we propose a distributed dual-polarized MIMO architecture suitable for URT tunnel scenarios. Specifically, the channel model is based on spatial three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM), which takes into account the geometric distribution of URT tunnels and the cross-polarization effects between dual-polarized antennas. For dual-polarized MIMO systems, the polarized-aware sparse channel estimation (PASCE) method is proposed for effective channel estimation. Additionally, we derive closed-form expressions for the MMSE and MR precoding schemes. The polarized-aware dynamic interference cancellation (PADIC) algorithm is developed to eliminate in terference between different polarization modes and multiple users. The simulation results demonstrate that the proposed dual-polarized precoding algorithm can withstand high cross polarization correlation (XPC) and improve the efficiency of V2G communication to achieve high rates. |
| title | Dual-Polarized Massive MIMO Based on Precoding for Vehicle-To-Ground Communication in Urban Rail Transit |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.25217 |