Near-Field Sensing Enabled Predictive Beamforming: Fundamentals, Framework, and Opportunities

TL;DR

This paper introduces a near-field predictive beamforming framework that leverages full-dimensional location sensing to improve beamforming for high-mobility users, offering proactive, low-complexity, and generalizable solutions.

Contribution

The paper presents a novel near-field predictive beamforming framework that enables mobility-aware, prior-knowledge-free, and low-complexity beam design for high-mobility wireless networks.

Findings

Case study confirms performance benefits of the proposed framework.

Framework enables full-dimensional location and velocity sensing.

Proposes new opportunities for near-field wireless communication research.

Abstract

The article proposes a novel near-field predictive beamforming framework for high-mobility wireless networks. Specifically, due to the spherical waves and non-uniform Doppler frequencies brought by the near-field region, the new ability of full-dimensional location and velocity sensing is characterized. Building on this foundation, the near-field predictive beamforming framework is proposed to proactively design beamformers for mobility users following arbitrary trajectories. Compared to the conventional far-field counterpart, the near-field predictive beamforming stands out due to: i) Prior-Knowledge-Free Prediction, and ii) Low-Complexity and Generalizable System Design. To realize these advantages, the implementation methods are discussed, followed by a case study confirming the benefits of the proposed framework. Finally, the article highlights promising research opportunities inspired by the proposed framework.