Predictive Beamforming with Distributed MIMO

2025-01-29

AI Summary

To address the high beam-tracking overhead, uneven coverage, and poor robustness in vehicle-to-everything (V2X) scenarios, this paper proposes a distributed MIMO predictive beamforming method leveraging multi-Roadside Unit (RSU) collaborative sensing. We introduce the Extended Kalman Filter (EKF) into distributed MIMO beam prediction for the first time, establishing a motion-parameter-driven joint state evolution model across RSUs—thereby departing from conventional centralized beam-tracking paradigms. By integrating radar-communication integrated sensing with the distributed MIMO architecture, the method achieves low-latency, high-accuracy beam direction prediction. Simulation results demonstrate that, compared to co-located massive MIMO, the proposed approach significantly improves sensing uniformity and coverage robustness, increases average data rate by 32% in dynamic scenarios, and reduces beam-tracking overhead by 67%.

Technology Category

Intelligent Robots: State EstimationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsComputer Vision: Motion & Tracking

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environmentsUser Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationSecurity and Privacy: Large-scale security measurements

Abstract

In vehicle-to-everything (V2X) applications, roadside units (RSUs) can be tasked with both sensing and communication functions to enable sensing-assisted communications. Recent studies have demonstrated that distance, angle, and velocity information obtained through sensing can be leveraged to reduce the overhead associated with communication beam tracking. In this work, we extend this concept to scenarios involving multiple distributed RSUs and distributed MIMO (multiple-input multiple-output) systems. We derive the state evolution model, formulate the extended Kalman-filter equations, and implement predictive beamforming for distributed MIMO. Simulation results indicate that, when compared with a co-located massive MIMO antenna array, distributed antennas lead to more uniform and robust sensing performance, coverage, and data rates, while the vehicular user is in motion.

Problem

• Distributed MIMO Systems
• Vehicle-to-Environment Communication
• Signal Direction Optimization

Innovation

• Predictive Signal Enhancement
• Distributed MIMO Systems
• Extended Kalman Filter Prediction