Tail-Aware Unsupervised Learning for Wideband Terahertz Quantized Beamforming in Sensor Data Transmission
Year: 2026
Type: article
Abstract: Data-intensive sensor networks require high-capacity wireless links to transmit sensor measurements. However, in wideband analog beamforming, beam squint reduces array gain, while imperfect channel state information (CSI) and finite-resolution phase shifters further degrade spectral efficiency (SE). To address these challenges, this paper proposes a center-anchored reliability-aware and tail-aware quantized beamforming (CRTQ-BF) algorithm for wideband terahertz sensor-data transmission over single-user multiple-input single-output (MISO) links with one radio-frequency (RF) chain. CRTQ-BF comprises three modules: the center-anchored reliability-aware refinement module learns a wideband phase correction from CSI reliability and a center-subcarrier phase reference; the tail-aware scenario learning module increases the weight of low-SE CSI-perturbation scenarios while retaining the mean-SE objective; and the quantization-aware phase projection module applies hard finite-phase quantization before scenario SE evaluation. Simulation results show that with increased inference time, CRTQ-BF achieves a higher mean SE with fewer trainable parameters than the physically anchored beamforming neural network (BFNN) baseline and demonstrates good robustness to CSI estimation errors. Remote-estimation simulations further show that CRTQ-BF improves sensor-data delivery reliability and reduces remote state-estimation error.
Source: Sensors
DOI: 10.3390/s26217081
Authors: Jiangnan Xie, Shujuan Yu, Yun Zhang, Liya Huang
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225654791
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