arXiv subjects

Shuaiqi Gao

Publications and source records attributed to Shuaiqi Gao.

4 recordsLinked to original sources

arXiv2026

A Geometry Map-Based Site-Specific Propagation Channel Model for Urban Scenarios

With the rapid deployments of 5G and 6G networks, accurate modeling of urban radio propagation has become critical for system design and network planning. However, conventional statistical or empirical models fail to fully capture the influence of detailed geometric features on site-specific channel variances in dense urban environments. In this paper, we propose a geometry map-based propagation channel model that directly extracts key parameters from a 3D geometry map and incorporates the Uniform Theory of Diffraction (UTD) to recursively compute multiple diffraction fields, thereby enabling accurate prediction of site-specific large-scale path loss and time-varying Doppler characteristics in urban scenarios. A well-designed identification algorithm is developed to efficiently detect buildings that significantly affect signal propagation. The proposed model is validated using urban measurement data, showing excellent agreement of path loss in both line-of-sight (LOS) and nonline-of-sight (NLOS) conditions. In particular, for NLOS scenarios with complex diffractions, it outperforms the 3GPP and simplified models, reducing the RMSE by 7.1 dB and 3.18 dB, respectively. Doppler analysis further demonstrates its accuracy in capturing time-varying propagation characteristics, confirming the scalability and generalization of the model in urban environments.

eess.SP↗

arXiv2026

Deep Learning-Based Site-Specific Channel Modeling and Inference

Site-specific channel inference plays a critical role in the design and evaluation of next-generation wireless communication systems by considering the surrounding propagation environment. However, traditional methods are unscalable. Recently, satellite imagery has emerged as a valuable modality containing rich propagation information for AI-based channel prediction. However, existing approaches using these images are limited to predicting large-scale fading parameters, lacking the capacity to reconstruct the complete channel impulse response (CIR). To address this limitation, we propose a deep learning-based site-specific channel modeling and inference framework using satellite images to predict structured Tapped Delay Line (TDL) parameters. We first establish a joint channel-satellite dataset based on measurements. Then, a novel deep learning network is developed to reconstruct the channel parameters. Specifically, a cross-attention-fused dual-branch pipeline extracts macroscopic and microscopic environmental features, while a recurrent tracking module captures the long-term dynamic evolution of multipath components. Experimental results demonstrate that the proposed method achieves high-quality reconstruction of the CIR in unseen scenarios, with a Power Delay Profile (PDP) Average Cosine Similarity exceeding 0.96. This work provides a pathway toward site-specific channel inference for future dynamic wireless networks.

eess.IV↗

arXiv2025

Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

5G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non stationarity. Key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes are also obtained. Finally, accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design, optimization, and link-level simulation and validation of 5G-R systems.

cs.IT↗

arXiv2024

Cluster-Based Time-Variant Channel Characterization and Modeling for 5G-Railways

With the development of high-speed railways, 5G for Railways (5G-R) is gradually replacing Global System for the Mobile Communications for Railway (GSM-R) worldwide to meet increasing demands. The large bandwidth, array antennas, and non-stationarity caused by high mobility has made 5G-R channel characterization more complex. Therefore, it is essential to develop an accurate channel model for 5G-R. However, researches on channel characterization and time-variant models specific to 5G-R frequency bands and scenarios is scarce. There are virtually no cluster-based time-variant channel models that capture statistical properties of 5G-R channel. In this paper, we propose a cluster-based time-variant channel model for 5G-R within an enhanced 3GPP framework, which incorporates time evolution features. Extensive channel measurements are conducted on 5G-R private network test line in China. We then extract and analyze typical channel fading characteristics and multipath cluster characteristics. Furthermore, birth-death process of the clusters is modeled by using a four-state Markov chain. Finally, a generalized clustered delay line (CDL) model is established in accordance with 3GPP standard and validated by comparing the results of measurements and simulations. This work enhances the understanding of 5G-R channels and presents a flexible cluster-based time-variant channel model. The results can be used in the design, deployment, and optimization of 5G-R networks.

cs.IT↗