Dual-Polarized Massive MIMO Based on Precoding for Vehicle-To-Ground Communication in Urban Rail Transit

Researchers from Beijing Jiaotong University have published a paper on arXiv proposing a new dual-polarized massive MIMO system designed to dramatically improve vehicle-to-ground (V2G) communication in urban rail transit (URT) tunnels. The system, which is currently under review for IEEE TVT, addresses a critical bottleneck: existing URT communication networks struggle to handle the massive data exchange required for intelligent, diversified services like real-time video surveillance, passenger information systems, and autonomous train control.

The proposed solution is a distributed dual-polarized MIMO architecture that leverages a spatial 3D non-stationary geometry-based stochastic model (GBSM) to accurately capture the unique propagation environment of URT tunnels, including cross-polarization effects. Key innovations include a polarized-aware sparse channel estimation (PASCE) method for efficient channel estimation and a polarized-aware dynamic interference cancellation (PADIC) algorithm to eliminate interference between different polarization modes and multiple users. Closed-form expressions for MMSE and MR precoding schemes were also derived. Simulation results demonstrate that the dual-polarized precoding algorithm can withstand high cross-polarization correlation (XPC) and significantly improve V2G communication efficiency.