Problem
Research questions and friction points this paper is trying to address.
near-field
far-field
UM-MIMO
signal processing
propagation
Learning-based near- versus far-field boundaries for ultra-massive MIMO communications
🤖 AI Summary
本文提出一种无监督学习框架，通过接收信号测量区分近场与远场传播，以实现超大规模MIMO系统的高效波束成形和信道估计。
📝 Abstract
Signal processing techniques for wireless communications and sensing fundamentally differ between near-field and far-field propagation regimes. Accurately identifying the applicable propagation region is therefore essential for enabling efficient beamforming and channel estimation in ultra-massive MIMO (UM-MIMO) systems. This paper proposes a fully unsupervised learning framework to distinguish near-field from far-field propagation based solely on received signal measurements, before estimating the communication distance, and without relying on channel state information. The proposed approach exploits spatial signal power variations across subarrays of a UM array as a physics-inspired feature extraction stage, followed by the OPTICS clustering algorithm to infer the communication region. Simulation results under various system configurations and signal-to-noise ratio (SNR) levels demonstrate that the proposed method accurately identifies the near-field and far-field regions, showing agreement with theoretical boundaries.
Innovation
Methods, ideas, or system contributions that make the work stand out.
unsupervised learning
signal power variations
OPTICS clustering
near-field and far-field
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