Physically Consistent Evaluation of Commonly Used Near-Field Models

📅 2026-02-11
📈 Citations: 0
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🤖 AI Summary
This work addresses the lack of physical consistency in commonly adopted simplified models for near-field multi-antenna communications, whose applicability in real systems remains questionable. For the first time, a physically consistent near-field reference model grounded in electromagnetic field theory is established, enabling a systematic evaluation of the accuracy of representative simplified models. The results reveal that while these simplified models can support basic beam focusing, they exhibit significant deviations in sidelobe structure and frequency response characteristics, exposing their fundamental limitations in complex scenarios. This study provides a reliable benchmark for near-field modeling and clearly delineates the operational boundaries within which existing approaches remain valid.

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📝 Abstract
Near-field multi-antenna wireless communication has attracted growing research interest in recent years. Despite this development, most of the current literature on antennas and reflecting structures relies on simplified models, whose validity for real systems remains unclear. In this paper, we introduce a physically consistent near-field model, which we use to evaluate commonly used models. Our results indicate that common models are sufficient for basic beamfocusing, but fail to accurately predict the sidelobes and frequency dependence of reflecting structures.
Problem

Research questions and friction points this paper is trying to address.

near-field
multi-antenna
wireless communication
reflecting structures
physical consistency
Innovation

Methods, ideas, or system contributions that make the work stand out.

physically consistent modeling
near-field communication
multi-antenna systems
reflecting structures
beamfocusing
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Georg Schwan
ETH Zurich, Switzerland
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Alexander Stutz-Tirri
ETH Zurich, Switzerland
Christoph Studer
Christoph Studer
Professor at ETH Zurich
VLSI circuits and systemswireless communicationsignal processingoptimizationmachine learning