Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Mingxu Wang; Jianjun Yu; Xianming Zhao; Jiali Chen; Ye Zhou; Xin Lu; Hansong Ma; Chengzhen Bian; Wen Zhou; Kaihui Wang; Weiping Li; Iman Tavakkolnia

arXiv · 2609.32682

Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Mingxu Wang·Jianjun Yu·Xianming Zhao·Jiali Chen·Ye Zhou·Xin Lu·Hansong Ma·Chengzhen Bian·Wen Zhou·Kaihui Wang·Weiping Li·Iman Tavakkolnia

View original source ↗Read source document ↗Export metadata ↓

Abstract

Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mingxu Wang, Jianjun Yu, Xianming Zhao, Jiali Chen, Ye Zhou, Xin Lu, Hansong Ma, Chengzhen Bian, Wen Zhou, Kaihui Wang, Weiping Li, Iman Tavakkolnia. 2026-09-26. Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration. https://arxiv.org/abs/2609.32682

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Symmetric Location Problem: a Song of Efficiency and Robustness

The aim of this Lecture Note is to introduce the Signal Processing (SP) community to a powerful yet still under-utilised tool: the semiparametric statistics. In short, the semiparametric framework allows us to estimate or perform hypothesis testing on a finite-dimensional parameter $\boldsymbolθ \in Θ\subseteq \mathbb{R}^p$ in the presence of an \textit{infinite-dimensional nuisance parameter} $g \in \mathcal{S}$ (i.e. a function), such as the density of the noise. Clearly, this framework is general enough to include almost every SP application. Remarkably, as the title suggests drawing on George R. R. Martin's famous book series, the greatest advantage of semiparametric statistics over parametric and non-parametric ones lies in the fact that it is able to reconcile two seemingly dichotomous concepts: statistical efficiency and distributional robustness. To explain exactly what this means, in this Lecture Note we will focus our attention on the famous and fundamental symmetric location problem.

eess.SP↗

arXiv2026

Single-Voxel Wireless NeRF for Spatial Spectrum Prediction

Wireless channel measurements across multiple spatial directions are crucial for AI-driven applications, such as RF digital twins and integrated communication and sensing. However, collecting channel data across large scenes is labor-intensive. Wireless NeRFs address this challenge by learning propagation behavior from sparse measurements and synthesizing channel spatial spectrum magnitude at unseen locations. However, existing wireless NeRFs inherit dense volumetric sampling from vision NeRFs, which requires substantial computation. This paper asks whether such dense sampling is necessary for predicting magnitudes of the wireless spatial spectrum. We empirically show that wireless NeRFs are over-parameterized for this task and introduce SV-INGP, a sparse volumetric sampling variant of Instant Neural Graphics Primitives (INGP). Across real-world and simulated datasets, SV-INGP matches the median Structural Similarity Index Measure (SSIM) of the NeRF2 baseline while reducing training time by 184x. These results generalize across LoS and NLoS scenes, sub-6 and millimeter-wave frequencies, and antenna array tapering configurations, suggesting a simpler and more efficient design path for RF digital twins

eess.SP↗

arXiv2026

Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning

Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are highly susceptible to artifacts, volume conduction, low signal-to-noise ratio, and substantial inter-subject variability. This chapter provides a practical and methodological guide to modern EEG analysis, spanning EEG preprocessing, artifact removal, filtering, bad-channel detection and interpolation, re-referencing, independent component analysis (ICA), and preprocessing of simultaneous EEG-fMRI recordings. We review major approaches for computational EEG analysis, including event-related potentials (ERPs), time-frequency analysis, functional and effective connectivity, source localization, multivariate decoding, permutation testing, and multiple-comparison correction. We then examine machine-learning methods for EEG, from feature-based classifiers to deep learning and emerging EEG foundation models, with emphasis on cross-subject generalization, limited-data regimes, data leakage, evaluation metrics, and fair benchmarking. Reproducibility is treated as a core requirement throughout, including transparent preprocessing, BIDS-EEG data organization, standardized derivatives, preservation of raw data, and FAIR data practices. The chapter is intended as a practical reference for researchers developing reliable, interpretable, and reproducible EEG analysis and machine-learning pipelines.

eess.SP↗