Predictor Antenna Systems: Exploiting Channel State Information for Vehicle Communications

TL;DR

This paper explores predictor antenna systems in 5G vehicle communications, focusing on how to improve channel state information prediction to enhance QoS amidst real-world challenges.

Contribution

It analyzes resource allocation strategies for predictor antenna systems considering practical issues like spatial mismatch and CSI estimation errors.

Findings

Resource allocation schemes can mitigate spatial mismatch effects.

CSI prediction accuracy improves with optimized antenna placement.

Practical constraints significantly impact predictor antenna system performance.

Abstract

Vehicle communication is one of the most important use cases in the fifth generation of wireless networks (5G). The growing demand for quality of service (QoS) characterized by performance metrics, such as spectrum efficiency, peak data rate, and outage probability, is mainly limited by inaccurate prediction/estimation of channel state information (CSI) of the rapidly changing environment around moving vehicles. One way to increase the prediction horizon of CSI in order to improve the QoS is deploying predictor antennas (PAs). A PA system consists of two sets of antennas typically mounted on the roof of a vehicle, where the PAs positioned at the front of the vehicle are used to predict the CSI observed by the receive antennas (RAs) that are aligned behind the PAs. In realistic PA systems, however, the actual benefit is affected by a variety of factors, including spatial mismatch, antenna utilization, temporal correlation of scattering environment, and CSI estimation error. This thesis investigates different resource allocation schemes for the PA systems under practical constraints.