Ultra massivo mimo systems identify near field and far field regions
Learning-based near- versus far-field boundaries for ultra-massive MIMO communications
Information Theory
Summary
Wireless signals behave differently depending on whether devices are close or far apart, which affects how signals are sent and received. The authors created a new way to automatically tell if a device is in the near or far range just by listening to the signals, without needing extra information about the channel. They use patterns in signal strength from parts of a large antenna array and group these patterns to figure out where the communication is happening. Their tests with computer simulations show this method works well under different conditions and matches what theory predicts.
What this means in practice
- •For wireless network engineers: Automatically identify near-field or far-field propagation regions from raw signal data to improve beamforming and channel estimation in large antenna systems.
- •For signal processing developers: Incorporate physics-inspired signal variation features and clustering to detect propagation regions without needing channel state information.
Authors
Simon Tarboush, Nour Kouzayha, Hadi Sarieddeen, Tareq Y. Al-Naffouri, Giuseppe Caire
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.