Aircraft cabin communication takes on a key role in communication networks. Due to the increasing demand for high data rates among passengers, millimetre-wave (mmWave) and sub-Terahertz (sub-THz) bands, with abundant spectrum resources, are envisioned as promising options. Therefore, detailed channel measurements are required to understand significant channel characterization in aircraft cabins. This paper presents a comprehensive comparison and analysis of channel characterization at 28 GHz, 38 GHz, and 130 GHz based on extensive measurements conducted in an aircraft cabin. A total of 84 transmitter-receiver (Tx-Rx) positions are measured, covering both line-of-sight (LoS) and non-LoS conditions, with Tx-Rx distance ranging from 1 m to 10 m. Based on the measured data, both large-scale and small-scale channel characterization parameters are extracted and analyzed. Statistical models of path loss, shadow fading, Rician K-factor, root-mean-square (RMS) delay spread, and RMS angular spread are proposed. The characterization is analyzed using the power-delay-angular profile and power-angular spectrum. The multipath components are clustered using the density-based spatial clustering of applications with noise algorithm to analyze their intra-cluster delay spread and intra-cluster angle spread. In addition, this paper analyses the system capacity and outage probability, providing some basis for communication system design and planning. To the best of our knowledge, this paper is the first to have both mmWave and sub-THz measurements and analysis performed on an aircraft cabin.

With the advent of 6G networks, integrated sensing and communication (ISAC) is a key enabling technology for native perception in complex industrial Internet of Things (IoT) environments, which demands accurate ISAC channel models. However, most existing models mainly capture correlation at the cluster level, making it difficult to characterize finer-grained parameter relationships and intra-cluster variations, and lack an effective mechanism for bidirectional parameter coupling between communication and sensing channels in industrial IoT scenarios. This paper proposes a shared-cluster geometry-based stochastic ISAC (SC-GBSM-ISAC) channel model for industrial IoT. The model is parameterized using channel measurements at 38 GHz and 132 GHz in a high-clutter indoor factory (InF (A)) scenario. Compared with existing ISAC channel models, the proposed model has the following features. First, a closed-loop “sensing–communication–sensing” channel modeling framework is constructed based on 3GPP TR 38.901, which integrates bidirectional parameter interaction between communication and sensing channels. Second, by selectively reusing sensing-target clusters to construct shared clusters for communication-channel modeling, the model introduces a physically consistent shared-scattering structure while preserving the statistical characteristics specified in 3GPP TR 38.901. Third, by combining scatterer information inferred from the communication channel with a scenario-dependent sensing probability model, the sensing background component is reconstructed. Finally, measurement-simulation comparisons in an independent low-clutter InF (B) scenario demonstrate that the proposed model effectively reproduces bidirectional parameter coupling and primary spatio-temporal statistical metrics at 38/132 GHz, validating its accuracy and cross-scenario consistency in the measured scenarios.

Sub-terahertz (sub-THz) channel modeling for industrial Internet of Things (IIoT) is hindered by scarce measurements and strong scattering from metallic machinery. This letter proposes Geo-CGAN, a geometry-aware conditional GAN that synthesizes physically consistent power–delay–angular profiles (PDAPs) by injecting a geometric condition vector into both the generator and the discriminator. A two-stage transfer-learning strategy pretrains Geo-CGAN on abundant 28-GHz ray-tracing data and fine-tunes it on limited 220-GHz measurements with regularized adaptation. Measurements in a factory workshop show that Geo-CGAN achieves an average structural similarity index measure (SSIM) of 0.725. It also better matches the statistics of delay spread, angular spread, and path loss than TT-GAN and GAN-GRU, demonstrating proof-of-concept performance with limited measurements.

Single-AP passive UWB localization systems suffer from positioning accuracy degradation caused by unfavorable node geometry, UE localization errors, and AP–UE–TAG collinearity. To address these issues, this paper proposes a heuristic path-planning algorithm with GDOP optimization and lightweight neural network assistance. A multi-factor loss function integrating GDOP, Euclidean distance, motion continuity, and collinearity avoidance is constructed to guide UE movement toward favorable geometric configurations. A lightweight fully connected neural network (832 parameters, <1 ms inference) is designed to rapidly predict the optimal movement direction, forming a closed-loop ‘localization–planning–optimization–update’ framework. Simulation and real-world experiments demonstrate an average TAG localization error of 0.17 m, a positioning success rate of 99%, and an 18.35% reduction in computational time compared to the pure heuristic approach.

This paper presents gradient-enhanced non-dominated sorting genetic algorithm II (G-NSGA-II) to address the challenges of local optima and solution non-uniqueness in the complex permittivity extraction problem for the first time. This adaptive hybrid algorithm integrates the global exploration capability of NSGA-II with gradient-based local refinement, triggered by a population-stagnation detection mechanism. Furthermore, multi-dimensional constraints are incorporated by jointly optimizing transmission and reflection coefficients across multiple sample thicknesses. Experimental validation conducted on six typical polymers in the 20–40 GHz band demonstrates that the retrieved relative permittivity and thicknesses are in high agreement with literature values and physical measurements. Compared to standard heuristic and gradient-based algorithms, the proposed G-NSGA-II reduces the number of generations required for convergence by approximately 50%. This significant improvement in speed, combined with enhanced robustness, provides a highly reliable and efficient solution for broadband dielectric characterization in architectural and electromagnetic engineering. The simple measurement method and the proposed efficient algorithm allow for a rapid evalutaion of wireless performance within indoor environments. This approach serves as a valuable tool for optimizing existing wireless layouts and improving network performance.

Accurate trajectory prediction is essential for autonomous driving systems to make safe and efficient decisions. Traditional global message-passing methods, though effective at capturing mutual interactions, suffer from an O(N-2) parameter complexity, which limits their scalability in high-density traffic environments. To address this, we propose a message-passing approach based on local neighborhoods, which reduces the complexity to O(N K-max) by restricting each node's interactions to its most relevant neighbors. On the Argoverse 1 motion forecasting benchmark, our model achieves a minADE(6) of 0.739 and a minFDE(6) of 1.133 with only 1.56M parameters, improving both metrics over a global message-passing baseline. On Argoverse 2, it attains a minFDE(6) of 1.196 and an MR6 of 12.2. These results demonstrate that local neighborhood message passing can simultaneously enhance prediction accuracy and computational efficiency, offering a scalable and practical solution for motion prediction in autonomous driving systems.

As essential radiating elements in RF and microwave microsystems, microstrip antennas require sufficient bandwidth to ensure stable operation, integration flexibility, and overall microsystem performance. From a microsystem optimization perspective, this paper proposes a bandwidth extension method for microstrip antennas that combines swarm intelligence and reinforcement learning. The proposed ICOA-TD3 framework is designed to enhance antenna bandwidth within target frequency bands and thus improve the performance robustness of compact RF microsystems. In the proposed method, an improved crayfish optimization algorithm (ICOA) is first used to explore the global design space and achieve global bandwidth enhancement, followed by the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for local refinement and further exploitation of the antenna structure’s bandwidth potential. In Experiment 1, the impedance bandwidth (S11≤−10dB) is increased by up to 200%. In Experiment 2, the impedance bandwidth (S11≤−10dB) and axial-ratio (AR) bandwidth (AR≤3dB) are improved by up to 27% and 250%, respectively. The results indicate that the proposed method is a feasible solution for bandwidth-oriented optimization of microstrip antennas and is promising for the intelligent design of high-performance RF microsystems.

This paper presents a detailed measurement campaign and a comprehensive analysis of 15 GHz ultra-massive multiple-input multiple-output (UM-MIMO) channels tailored for the urban microcell (UMi) environment. Channel sounding is performed over 14.875-15.125 GHz using a time-domain platform comprising a 128-element L-shaped transmit array and a 64-element square receive array. Four representative scenarios are investigated, namely near-field line-of-sight (LoS), near-field foliage-shaded, far-field foliage-shaded, and far-field LoS street canyon scenarios, resulting in 81 distinct transmit-receive links. Based on the measured data, conventional channel characteristics, including path loss, power delay angle profiles, delay spread, and angular spread, are characterized, while UM-MIMO-specific phenomena associated with near-field effects, spatial non-stationarity (SNS), and channel hardening (CHD) are quantitatively analyzed. Channel capacity is further evaluated to reveal the effects of different UMi propagation conditions on system performance. The reported results provide empirical support for the new mid-band spectrum (6-24 GHz, including Frequency Range 3 (FR3)) UM-MIMO channel modeling and offer practical guidance for the design and deployment of future sixth-generation (6G) microcell networks.

Visual Simultaneous Localization and Mapping (VSLAM) is the key technology for autonomous navigation of mobile robots. However, feature-based VSLAM systems still face two major challenges in dynamic complex environments: insufficient feature reliability and significant dynamic interference, urgently requiring improved matching robustness. This paper innovatively proposes a dynamic adaptive VSLAM system based on the High-repeatability and High-reliability feature matching network (2HR-Net), which improves localization accuracy in dynamic environments through three key innovations: First, the 2HR feature detection network is designed, integrating the K-Means clustering algorithm into L2-Net to achieve feature point detection with both high repeatability and high reliability. Second, the lightweight YOLOv8n model is integrated to detect and remove feature points in dynamic regions in real-time, effectively reducing the impact of dynamic interference on pose estimation. Finally, the shared matching Siamese network with a unique dual-branch feature fusion strategy and similarity optimization algorithm is proposed to enhance the accuracy of feature matching. The proposed algorithm was ultimately validated using the publicly available TUM dataset. The experimental results show that the feature detection method proposed in this paper achieved a repeatability rate of approximately 70% in various dynamic scenarios, which is significantly higher than traditional methods (such as ORB-SLAM3), whose repeatability typically falls below 40%. In addition, compared with ORB-SLAM3, the root mean square error (RMSE) and standard deviation (S.D.) of the Absolute Trajectory Error (ATE) in various dynamic scenarios were reduced by approximately 90%, indicating higher localization accuracy and stability. Therefore, the experimental results demonstrate that the proposed method outperforms mainstream methods such as ORB-SLAM3 in terms of feature repeatability, matching accuracy, and localization precision, providing an effective solution for robust VSLAM in dynamic environments.

To address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was constructed based on feature selection theory and evolutionary computation principles. The crossover and mutation operations of channel characteristics were executed by the genetic algorithm (GA), and the optimal feature parameters with high scenario relevance were selected using the minimum redundancy maximum relevance (mRMR) criterion. These parameters were then inputed into a backpropagation neural network model. To validate the method, a dataset containing 12 channel features was constructed with 1 745 groups of terahertz channel simulation data collected from indoor scenarios, and the model was trained and rigorously validated based on this dataset. The results demonstrate that the proposed algorithm improves accuracy and efficiency by 8% and 38.8%, respectively, and outperforms traditional algorithms in terms of convergence and transfer generalization capabilities.

Orbital angular momentum (OAM) communication is envisioned as a key technology for sixth generation wireless systems (6 G) and can be combined with terahertz (THz) communication technology to significantly enhance spectral efficiency. The measurement and accurate analysis of the channel provide an important foundation for terahertz OAM communication technology. Based on this, this paper first proposes a path loss model for OAM waves. Then, a terahertz OAM measurement platform is constructed in indoor scenarios, and a spiral phase plate (SPP) is used to generate multi-mode OAM waves at 132 GHz. Finally, the large-scale characteristics of the measured channel data are analyzed. The results show that, compared with the free space propagation loss model, this model better explains the wave loss characteristics of vortex waves, and the prediction accuracy is improved by an average of 11.8%.

The semantic segmentation is a critical task in LiDAR point cloud processing. Leveraging temporal information to provide contextual data for regions with low visibility or sparse observations has recently become a popular research direction, especially in autonomous driving. Existing methods, however, are often over-reliant on past frames, leading to cumulative errors (drift) caused by unconstrained frame-by-frame stacking. This paper proposes a dynamic alignment of historical frame memory information to ensure consistency with the observations of the current frame, reducing deviations caused by viewpoint changes or object movements and ensuring more accurate capture of current frame features. In addition, a new multi-scale feature fusion method, to the best of our knowledge, was introduced using the spatiotemporal (ST) method to extract the ST features, which reduces the inconsistencies between 2D range image coordinates and 3D Cartesian outputs. This approach enhances feature representation by optimizing and fusing the aligned channel features. This method was evaluated on the SemanticKITTI and SensatUrban datasets. The experimental results showed that it outperforms existing state-of-the-art methods regarding accuracy.

To address the shortcomings of multipath clustering algorithms in terahertz channel modeling, particularly in terms of multidimensional parameter adaptability and unsupervised feature separation, a variational autoencoder-based latent space multipath clustering (VAE-LMC) model was proposed. Firstly, the variational autoencoder (VAE) was utilized to learn latent representations of multipath delays and arrival angles, enhancing feature separability. Secondly, K-Means clustering was embedded into the VAE framework, with joint optimization of reconstruction loss, KL divergence, and clustering loss functions to resolve the challenges of feature separation in unsupervised learning. Finally, multipath clustering was performed in the latent space, and the results were mapped back to the real data space. Terahertz channel measurements at 129.5~135 GHz were conducted in a small factory scenario to construct training datasets and testing datasets. Experimental results demonstrate that the VAE-LMC model exhibits significant advantages in intra-cluster and inter-cluster characteristics, environmental consistency, and computational complexity, providing an efficient solution for terahertz channel multipath clustering in complex scenarios.

To address the critical gap in outdoor urban channel characterization for extremely-large-scale multiple-input multiple-output (XL-MIMO) systems in the under-explored 7-24 GHz bands, this work presents a novel channel sounder integrating time-domain measurement technique and heterogeneous real antenna array (RAA) architecture. The proposed sounder employs a 128-element L-shaped transmitter array and a four-sided 64-element receiver array, enabling 3D spatial coverage. The GPS-disciplined rubidium clock ensures nanosecond-level synchronization, while an anechoic-chamber-based over-the-air (OTA) calibration method reduces calibration time from tens of hours to minutes. The field measurements conducted in both line of sight (LoS) and non line of sight (NLoS) urban macro (UMa) scenario validate the effectiveness of the OTA calibration scheme, demonstrating stable clock synchronization (+/- 5 ns precision) and measurement accuracy in path loss. Results highlight the sounder's capability to support reliable spatiotemporal channel characterization for 6G XL-MIMO deployments in complex urban environments, thereby advancing channel modeling research and standardization efforts in the 7-24 GHz band.

This paper presents the extremely large-scale multiple-input multiple-output (XL-MIMO) channel measurements at 15 GHz in typical urban macro (UMa) scenarios. Channel characteristics, including the power-delay-angular profile (PDAP), delay spread, and angular spread, are studied based on the measured data. A comparative analysis of the channel characteristics is performed between line-of-sight (LOS) and non-line-of-sight (NLOS) environments, and the results are further compared with the standardized parameters in 3GPP TR 38.901. The extracted channel characteristics provide a valuable reference for 6G channel modeling and system-level performance evaluation.

Artificial neural networks (ANNs) have shown remarkable advantages in antenna modeling and optimization as surrogate models. However, in single-objective modeling, the convergence accuracy of ANN-based antenna models is often insufficient. This paper proposes an improved crayfish optimization algorithm (ICOA) to optimize the initial weights and biases of the neural network, aiming to enhance both convergence accuracy and speed. The improvement incorporates an elite opposition strategy and iterative local search while simplifying certain formulas in the original algorithm. The improved algorithm was evaluated on standard test functions, demonstrating superior convergence speed and accuracy compared to other algorithms. Subsequently, the ICOA was applied to the single-objective modeling of two microstrip patch antennas, predicting antenna return loss S11. The findings indicate that the proposed ICOA-ANN approach demonstrates superior modeling accuracy compared to both the COA-ANN and traditional ANN methods.

In wireless communications, dynamic time-varying channel modeling has long been a critical research focus. This paper presents an adaptive threshold dynamic cluster tracking algorithm based on the multipath component distance (MCD). The proposed algorithm first introduces a joint normalization metric for angle and delay in the MCD calculation, and enhances sensitivity and robustness to small angle and delay variations through geometric transformation. To enhance the coherence and physical consistency of cluster trajectories, an adaptive threshold selection strategy is developed based on cluster life cycle statistics, replacing the static evaluation approach of conventional cluster validity indicators (CVIs). Finally, using channel measurement data collected in indoor factory environments at 38 GHz and 132 GHz, the birth–death behaviours, correlation, and divergence between the communication and sensing channels are thoroughly analyzed, demonstrating the effectiveness and applicability of the proposed algorithm.

In order to enhance channel capacity (CC) and propagation distance, a new radio (NR) in sub-THz band (STB) is employed and propagated over an orbital angular momentum (OAM) fiber for delivering user data by OAMbased space-division multiplexing (SDM). For propagating the STB wave, A weakly-coupled optical fiber (WCOF) with four layers is designed and manufactured in our Lab for propagation OAM beams, where 6 OAM groups (OAMGs) can be supported with a larger difference of effective refraction index among different OAMGs. A propagation loss of less than 3.2 dBm/km for 6 OAMGs at 1550 nm is achieved in the proposed WOCF. A probative platform is also built for denoting the feasibility of the proposed scheme. Besides OAMG 0 without OAM, the OAM mode with an integral topological charge (TC) of -1/-2/-3/-4/-5 is utilized as the representative of OAMG 1/2/3/4/5 in the experiments, respectively. The captured intensities and interference patterns depict that the propagated five OAM beams can be successfully detected at receiving side, which shows that the proposed method is feasible. Bit error rates (BERs) and constellation plots (CPs) are also adopted for analyzing the performances of the proposed concept, respectively. When the SDM is employed or not, the measured BERs for five OAM channels with TC = -1, -2, -3, -4 and -5 degrade by 0.61/2.8, 0.83/3.21, 1.27/3.64, 1.63/4.05 and 1.98/4.39 dB compared with "BTB", respectively, where a less than 2/5 dB degradation without/with SDM can be achieved. A less than 3 dB degradation for five OAM modes between "W/O SDM" and "W/SDM" can be observed, which can be contributed to the crosstalk induced by SDM. In addition, the measured CPs of 16-quadrature amplitude modulation (QAM) in five OAM channels are consistent with the measured BER distributions at an optical signal to noise ratio of 15.2 dB. Moreover, the influences of data speed and fiber length on the BER distributions are also explored, where the BER of the monitored channel of TC = -5 decreases as the data speed or fiber length increases. Compared to the data rate, the fiber length is more sensitive to fiber length. Although, the propagation loss can be degraded by reducing contrast of refraction index against the cladding (CRIC) between core layer and cladding for achieving a long-haul propagation, the number of OAM modes is also decreased. Consequently, the propagation distance and the supported number of OAM modes should be overall considered for balance in practical applications.

Integrated sensing and communication (ISAC) has been considered a promising technology in the sixth-generation (6G) system. An accurate and realistic wireless channel model is crucial for optimizing and evaluating ISAC systems and techniques. However, the ISAC channel characteristics have not been well understood, and sensing channels have not been well modeled in the existing standard-level channel models. In this article, extensive sensing and communication channel measurements are conducted in four representative indoor factory (InF) scenarios at 28, 38, and 132 GHz, in which over 2600 spatial channel impulse responses are collected. In light of the measurement results, physical parameters and insights in ISAC channels are comprehensively analyzed, including the temporal and spatial features, cluster-level characteristics, and correlation between the communication and sensing channels. Finally, a geometry-based stochastic model (GBSM) ISAC channel model combined sharing feature is proposed based on the 3GPP standard framework. Some special sensing properties, such as shared sensing clusters and sensibility probability, are novelly introduced to model the sensing channels. Simulation results demonstrate that the proposed ISAC channel model can be well-compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.

This paper presents an exhaustive comparison of channel measurements and appropriate channel statistics at 28 GHz, 38 GHz, and 130 GHz in the light of massive measurements conducted in an aircraft cabin environment. A total of 84 transmitter-receiver (Tx-Rx) positions are measured, covering both line-of-sight (LoS) and non-line-of-sight (NLoS) cases, with Tx-Rx distances ranging from 1 m to 10 m. The close-in and floating-intercept path loss models are presented for the cabin environment, and root-mean-square (RMS) delay spread (DS) and angular spread (AS) are compared and analyzed. The results indicate that the path loss exponent (PLE) is smaller than the free space PLE in the LoS aisle case over all measured frequencies. Moreover, the RMS DS and AS decrease as frequency increases in LoS and NLoS cases. This work can be applied to the design and optimization of the wireless communication system within the aircraft cabin.