Orthogonal time frequency space (OTFS) modulation is a robust candidate waveform for future wireless systems, particularly in high-mobility scenarios, as it effectively mitigates the impact of rapidly time-varying channels by mapping symbols in the delay-Doppler (DD) domain. However, accurate frame synchronization in OTFS systems remains a challenge due to the performance limitations of conventional algorithms. To address this, we propose a low-complexity synchronization method based on a coarse-to-fine deep residual network (ResNet) architecture. Unlike traditional approaches relying on high-overhead preamble structures, our method exploits the intrinsic periodic features of OTFS pilots in the delay-time (DT) domain to formulate synchronization as a hierarchical classification problem. Specifically, the proposed architecture employs a two-stage strategy to first narrow the search space and then pinpoint the precise symbol timing offset (STO), thereby significantly reducing computational complexity while maintaining high estimation accuracy. We construct a comprehensive simulation dataset incorporating diverse channel models and randomized STO to validate the method. Extensive simulation results demonstrate that the proposed method achieves robust signal start detection and superior accuracy compared to conventional benchmarks, particularly in low signal-to-noise ratio (SNR) regimes and high-mobility scenarios.

ABSTRACT The vacuum tube ultra‐high‐speed flying train (vactrain) is regarded as a promising candidate for future railway transportation, while the distinctive operating environment and ultra‐high‐speed conditions exact rigorous demands on the performance of the train‐to‐ground wireless communication system. For the optimal design of the communication system, a comprehensive understanding of channel characterisation is crucial, and an accurate channel model requires investigation. Therefore, in this paper, we propose a three‐dimensional (3‐D) multi‐source geometric‐based stochastic model (GBSM) for the vacuum tube scenario using leaky coaxial cables (LCX). The electric field distribution of LCX is deduced by equating the periodic slots to multiple magnetic dipole antennas. Based on this, the theoretical models for line‐of‐sight (LoS) and nonline‐of‐sight (NLoS) components of channel impulse response (CIR) are derived, respectively. Furthermore, the closed‐form expressions of the multi‐link spatial‐temporal correlation functions, encompassing the spatial cross‐correlation function (CCF) and temporal autocorrelation function (ACF), are further derived and examined at 900 and 1800 MHz. The simulation results indicate that in the vactrain scenario, the wireless channel exhibits nonstationary characteristics. Furthermore, at 1800 MHz, the channel correlation decreases more rapidly than at 900 MHz, and the stationary interval of the channel is shorter.

The cell-free massive multi-input multi-output (CF-mMIMO) is a promising technology for the sixth generation (6G) communication systems. Channel prediction will play an important role in obtaining the accurate CSI to improve the performance of CF-mMIMO systems. This paper studies a deep learning (DL) based joint space-time-frequency domain channel prediction for CF-mMIMO. Firstly, the prediction problems are formulated, which can output the multi-step prediction results in parallel without error propagation. Then, a novel channel prediction model is proposed, which adds frequency convolution (FreqConv) and space convolution (SpaceConv) layers to Transformer-encoder. It is able to utilize the space-time-frequency correlations and extract the space correlation for irregular AP deployments. Next, we generate simulated datasets with different sizes of service areas, UE velocities and scenarios, and utilize the correlation analysis and cross-validation to determine the optimal hyper-parameters. According to the optimized hyper-parameters, the prediction accuracy and computational complexity are evaluated based on simulated datasets. It is indicated that the prediction accuracy of the proposed model is higher than that of traditional models, and its computational complexity is lower than that of traditional Transformer model. After that, the impacts of space-time-frequency correlations and signal-to-noise ratio (SNR) on prediction accuracy are studied. Finally, realistic datasets in a high-speed train (HST) long-term evolution (LTE) network are collected to verify the prediction accuracy. The verification results demonstrate that it also achieves higher prediction accuracy compared with traditional models in the HST LTE network.

​​In a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, this paper proposes a joint sparse and low-rank channel estimation method via adaptive threshold. This method addresses the high overhead of cascaded channel estimation and the difficulty of fixed-threshold sparse recovery methods in adapting to dynamic signal-to-noise ratio (SNR). This method uses parallel factor (PARAFAC) decomposition to uncover the low-rank structure of the received signal tensor and employs an iterative algorithm that adaptively updates thresholds based on residual energy to perform channel reconstruction under an angular-domain sparse model. Simulation results show that the proposed method achieves good estimation accuracy under limited training overhead and across varying SNR conditions.

Channel state information (CSI) prediction, which can efficiently avoid the channel aging problem, will be essential for the design of next generation advanced transceiver. To predict wideband CSI, this paper investigates time-frequency joint channel prediction based on deep learning (DL) models. Firstly, single-step and multi-step ahead prediction problems for wideband CSI are constructed, and dataset generation methods are presented. Then, a novel time-frequency joint channel prediction model is proposed to predict the wideband CSI, which merges the convolutional neural network (CNN) and the encoder of Transformer with the probabilistic sparse self-attention mechanism. This model can capture the time-frequency correlation information hidden in the wideband CSI datasets and reduce computational complexity significantly compared with the traditional Transformer model. Simulated wideband CSI datasets are generated, and optimal hyper-parameters are determined by autocorrelation analysis, similarity analysis and cross-validation. The performance of the proposed model is evaluated in terms of prediction accuracy, robustness and computational complexity, and is compared with classical DL models. Finally, realistic wideband CSI datasets generated according to channel data acquisition in fifth-generation (5G) commercial networks are used to validate the proposed model.

In recent years, satellite internet has been widely recognized as a key component of future integrated space-air-ground networks. With advancements in satellite miniaturization and launch technologies, mega-constellations have become a growing trend. The increasing number of satellites in constellations presents challenges for interference analysis. This paper proposes a novel interference analysis method based on time-elevation interference spectrum. The proposed method can provide a more comprehensive analysis for NGSO mega-constellations by considering the aggregated dynamic interference under the time-elevation domain. The interference characteristics under different orbital inclination, orbital plane numbers, orbital height, and ground station latitude are analyzed. Furthermore, the probability distributions of interference are derived based on the joint distribution of satellites and ground stations. The outage probabilities and throughput are also analyzed to measure the system’s availability. Through the validation of STK and the Monte Carlo method, our method has high accuracy.

Satellite internet has been widely recognized as a key component of future integrated space-air-ground networks in recent years. As the scale of satellite internet expands, the frequency resources available for satellite communication are becoming increasingly scarce. In integrated space-air-ground networks, the frequency sharing between satellite networks and terrestrial cellular networks is an important trend. However, this inevitably leads to severe co-frequency interference (CFI) with characteristics of aggregation and time-varying. In this paper, an analysis model for time-varying aggregate interference is proposed. The time-varying probability distribution function (PDF) of CFI is analyzed in several co-existence scenarios, and the interference sources are modeled to calculated the individual interference as well as the aggregate interference. Through simulation, the characteristics of each type of CFI are simulated and analyzed, and the simulation results are also validated, demonstrating the accuracy of the proposed model.

Channel measurements are the prerequisite for applying emerging transmission technologies and designing communication systems. Conventional time or frequency domain channel measurement methods cannot directly obtain Doppler information induced by high-mobility scenarios. The channel spreading function (CSF) simultaneously captures delay and Doppler information while naturally characterizing the propagation environment in the delay-Doppler (DD) domain. However, DD domain channel measurement methods remain underexplored. This paper presents an orthogonal time frequency space (OTFS) waveform-based DD domain channel measurement method for high-mobility scenarios. A native OTFS waveform, employed as the sounding signal, is designed for the first time, and its sounding capability is comprehensively analyzed. Next, we detail the methodology of DD domain channel measurement, including synchronization and CSF estimation. To enhance measurement precision, a joint fractional delay and Doppler shift estimation algorithm is proposed, and the overall performance of the proposed method is evaluated. Subsequently, a practical DD domain channel measurement system is established, followed by system calibration and verification. Finally, DD domain channel measurements are conducted in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scenarios. Measurement results, including the CSF and other small-scale fading characteristics, confirm the effectiveness of the proposed method and offer valuable insights for advancing research on high-mobility communications.

The proliferation of vehicular networks paves the way to the future intelligent transportation system. In this paper, we propose a novel non-stationary vehicle-to-vehicle (V2V) wideband channel model with the aid of environment construction including in-lane cars' mobility and roadside obstacles, which can emulate the dynamic evolutionary V2V environments. Regarding the in-lane cars, they are abstracted into clusters and their mobility behaviors including acceleration/deceleration car-following and lane-changing motions, are modeled based on the Krauss model and Bezier curve, respectively. Besides, the blockage of line-of-sight (LoS) links by other cars is computed by the knife-edge diffraction theory. In terms of the roadside static obstacle clusters, their spatial positions are characterized by the Poisson point process (PPP) located within the maximal radio coverage, defined by the ellipsoid region restricted by the maximal propagation delay. The roadside cluster generation/recombination process is affected by transceivers' relative speed difference and distance-related link probability. On this basis, the dynamic evolutionary non-stationary V2V channel impulse response (CIR) is obtained and the corresponding power density profiles in the space-time-frequency domains are realized by using the signal reconstructing theory. In the simulation section, the impacts of some key parameters, like vehicular speeds and obstacle intensity factor, on the channel characteristics are sufficiently explored. Furthermore, we verify our proposal based on actual measured data from the aspects of delay spread. Our proposal provides some assistance and insights into the optimization of vehicular communication systems.

This paper investigates narrow-beam channel characterization and performance evaluation for 5G for railway (5G-R) systems based on ray-tracing (RT) simulation. Three representative high-speed railway (HSR) scenarios including viaduct, cutting, and station are established, and RT-based dynamic narrow-beam channel simulations are conducted using a designed beam tracking scheme that ensures continuous alignment with the moving train. The channel characteristics are analyzed in terms of both large-scale and small-scale fading, as well as non-stationarity, providing statistical insights into path loss, shadow fading, fading severity, time-frequency-space dispersion, and stationarity interval. The influence of beamwidth on these channel properties is also examined. Furthermore, the performance of 5G-R systems operating in such narrow-beam channels is evaluated using the Vienna 5G simulator, with a focus on block error rate, throughput, and spectral efficiency. A hardware-in-the-loop simulation platform is developed to further assess synchronization signal reference signal received power, signal-to-interference-plus-noise ratio, and reference signal received quality. The results provide valuable guidance for the design and optimization of 5G-R systems in HSR environments.

Low earth orbit (LEO) satellites have the characteristics of low communication delay, low deployment cost, and wide coverage, which have become an important component of the 6G air-space-ground integrated information network. However, satellite-ground communication has a large propagation distance, complex fading, and fast terminal movement speed, causing the channel characteristics different from terrestrial communication networks. Therefore, channel modeling is necessary when deploying a satellite-ground communication network. In this paper, a 3D geometry-based stochastic model (GBSM) is proposed for satellite-ground communication links. The proposed channel model includes several environments such as urban, suburban, and rural. Based on this model, the channel impulse response (CIR) can be obtained, and the closed-form expression of spatial-temporal correlation function and Doppler power spectrum density are derived. Through simulation, the characteristics of large-scale fading and small-scale fading are analyzed, which depict the significant differences from the terrestrial networks. The relevant results can provide contributions to the design of future satellite-ground communication systems.

Confronting the fundamental limitation of subcarrier orthogonality degradation caused by Doppler shifts in high-mobility multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, this paper investigates channel estimation in the delay-Doppler (DD) domain for multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) modulation systems characterized by quasi-static sparse channels. To overcome the high-complexity constraints, we propose a novel channel estimation network that synergizes the strengths of a residual network with squeeze-and-excitation (SE-ResNet) and a Transformer. This network deeply exploits channel correlation information from both spatial and temporal dimensions, collaboratively enhancing the representation learning of spatial and temporal features for more accurate channel state information (CSI) estimation. Simulation results demonstrate that the proposed algorithm achieves approximately 17 dB performance gain over traditional algorithms and significantly outperforms existing single deep learning (DL) models in estimation accuracy, effectively addressing the inherent limitations of unimodal architectures by joint spatial and temporal modeling. We also compare the normalized mean square error (NMSE) performance under varying velocities, spatial correlation coefficients and MIMO antenna configurations. This work not only validates the effectiveness of the spatial-temporal cooperative mechanism for enhancing channel estimation accuracy but also provides a low complexity scheme of channel estimation for high-mobility communication systems.

Low Earth Orbit(LEO)satellite has the characteristics of low communication delay,low deployment cost and wide coverage,and has become an important part of the construction of the future space earth integrated network.However,satellite communication has large end-to-end propagation distance,complex fading and fast terminal movement speed,thus the channel characteristics are very different from the terrestrial cellular network.Based on this,in order to have a more comprehensive understanding of the characteristics and channel model of LEO satellite-ground channel,the current standardization progress of the satellite-ground channel by the international standards organization are summarized,the fading characteristics of the satellite ground channel at different propagation positions are discussed,the existing important channel models are classified and shown according to the modeling method,and finally the prospects for future work are proposed.

This paper investigates the channel prediction model based on deep learning (DL) for cell-free massive multi-input multi-output (CF-mMIMO) systems. The prediction problems which utilizes both spatial and temporal correlation information are formulated. Combining the graph convolutional network (GCN) and the encoder of Transformer with the relative positional encoding, a novel spatial-temporal joint channel prediction model is proposed. A CF-mMIMO system is simulated and datasets are generated according to prediction problems. The hyper-parameters of the proposed model are determined by adjacent matrix generation, autocorrelation analysis and cross-validation. According to the spatial-temporal joint prediction performance evaluation, it is shown that the proposed model has higher prediction performance compared with the traditional DL models in the appropriate range of computational complexity.

In this paper, semideterministic multi-input multi-output (MIMO) channel modeling is proposed for a tunnel with two types of cross sections. To be more consistent with the MIMO propagation characteristics of the measured data, ray tracing (RT) is used to simulate the specular components (SCs) and a directed path channel modeling method based on directive scattering model (DSM) is used to simulate the diffuse components (DCs), which has an important contribution in MIMO channel capacity. The performance of the proposed model is compared in terms of delay domain, angle domain, and channel capacity. By analyzing the DSM and the propagation graph based on the Lambertian scattering model (LSM), it was demonstrated that the proposed method is more suitable in terms of comprehensive performance.

In this paper, we propose a grid map construction algorithm that utilizes limited bandwidth millimeter-wave (mmWave) radar to achieve high-precision environmental perception and mapping. We first employ mmWave radar to capture environmental data. By configuring the radar bandwidth, the radar's resolution is leveraged to update the grid map. Combining the Bresenham algorithm, we have achieved rapid and accurate modeling of the environment, resulting in the final grid map construction outcome. Subsequently, the angle of arrival (AOA) distribution in the environment is statistically analyzed and fitted, demonstrating the proposed method's effectiveness. Finally, a comparison with a baseline map reconstructed using LiDAR is performed. The algorithm's perception and reconstruction capabilities are evaluated using root mean square error (RMSE), Jaccard similarity, and code execution time.

Line-of-sight (LOS) or non-line-of-sight (NLOS) identification is of vital significance to the localization of mobile sensors in intelligent substations for power Internet of Things. This article investigates the LOS/NLOS identification in substation scenarios, based on deep learning networks and feature fusion methods. Channel measurement data in high-voltage substation environments with LOS and NLOS cases are collected, and both original and manually extracted channel features are obtained to generate data sets. A novel LOS/NLOS identification model is proposed, which employs a deep neural network and a self-attention network to separately learn the information contained in the manually extracted channel features and the original channel feature. This model also applies a hybrid fusion method to capture correlation between the channel features and mitigate data inundation risk caused by the dimension difference of input features. The results of performance evaluation show that the proposed model not only has the identification accuracy as high as 98.95%, but also possesses good noise robustness and acceptable computational complexity.

Fifth-generation new radio vehicle-to-everything (5G-V2X) communication is an emerging technology to support advanced use cases and higher automation levels in Internet of Vehicles. A comprehensive and accurate knowledge of narrowbeam channels plays a crucial role in the utilization of the 5G-V2X technique. This article focuses on measurement and characterization of vehicle-to-infrastructure (V2I) narrowbeam channel. A flexible narrowbeam channel measurement system using a phased-array antenna is designed and is employed to perform a series of 3.35-GHz channel measurements with different beam widths in highway primary road and auxiliary road. Based on the collected data, the fading characteristics of V2I narrowbeam channel, including path loss, shadow fading, and $K$ -factor, are extracted, analyzed, and then modeled. Then, the V2I narrowbeam channel dispersion in time-frequency-space domain is characterized, and the statistical models of root-mean-square (RMS) delay spread, RMS Doppler spread, and RMS angular spread are proposed. In addition, the V2I narrowbeam channel nonstationarity is discussed in terms of the stationarity interval and birth-death process of multipath components, and results of the Markov chain model with parameters like state transition probability matrix and steady-state probability are reported. The results can contribute to the design and evaluation of 5G-V2X technology.

The multiple-input multiple-output (MIMO)-enabled beamforming technology offers great data rate and channel quality for next-generation communication. In this paper, we propose a beam channel model and enable it with time-varying simulation capability by adopting the stochastic geometry theory. First, clusters are generated located within transceivers' beam ranges based on the Matern hardcore Poisson cluster process. The line-of-sight, single-bounce, and double-bounce components are calculated when generating the complex channel impulse response. Furthermore, we elaborate on the expressions of channel links based on the propagation-graph theory. A birth-death process consisting of the effects of beams and cluster velocities is also formulated. Numerical simulation results prove that the proposed model can capture the channel non-stationarity. Besides, the non-reciprocal beam patterns yield severe channel dispersion compared to the reciprocal patterns.