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.

With the rapid development of the Internet of Things (IoT), emerging microservice architectures support scalable and flexible deployment to handle a significant increase in latency-sensitive requests in resource-limited edge. Due to complex data dependencies and instance sharing among microservices, service deployment and request routing tightly coupled, which motivates a complex joint optimization problem. In this case, the difficulty of service orchestration is extremely enlarged when considering multi-instance modeling and fine-grained latency analysis for the strict quality of service (QoS). Furthermore, since the objectives of end-to-end response latency and energy consumption present an inherent conflicting relationship, carefully reconciling the trade-offs between them through efficient microservice orchestration is necessary, but significantly challenging. However, most previous work failed to propose suitable approaches and models to address the above difficulties. Therefore, this paper investigates the energy-efficient microservice orchestration for latency-sensitive applications in edge computing, aiming to minimize both latency and energy consumption. We first develop a multi-instance queuing network model that accurately captures data dependencies and enables detailed analysis of queuing, computing, and communication delays. Then, we propose the Energy-efficient Determination of Microservice Instances (EDMI) algorithm to derive the minimal required number of instances. Furthermore, we design a deep reinforcement learning-based orchestration method, Global-Reward Soft Actor-Critic (GRSAC), which integrates local reward signals into global feedback to mitigate sparse rewards. Finally, we provide theoretical analysis on time complexity and convergence. Experimental results show that our proposed algorithm significantly outperforms existing approaches in reducing both response latency and energy consumption.

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 a comprehensive measurement campaign and channel characterization analysis for 15 GHz extremely large multiple-input multiple-output (XL-MIMO) systems operating in foliage-blocked urban macro-cell (UMa) scenarios. Leveraging a custom-designed time-domain channel sounder with 128-element transmit and 64-element receive antenna arrays, we conducted empirical investigations across seven distinct vegetation-depth configurations (0.5-7 m) in real-world environments. The study systematically validated and enhanced two benchmark foliage attenuation models: COST 235 and ITU-R P.833-10. Through nonlinear least-squares fitting, the ITU-R model demonstrated superior adaptability to new mid-band frequencies, achieving a root mean square error (RMSE) of 0.42 dB across the measured vegetation depth range. Additionally, we compared the angular spread and delay spread for links with and without foliage blockage to evaluate the impact of foliage-induced channel degradation.

Text-to-image (T2I) models enable users worldwide to create high-definition and realistic images through text prompts, where the underrepresentation and potential misinformation of images have raised growing concerns. However, few existing works examine cultural representativeness, especially involving whether the generated content can fairly and accurately reflect global cultures. Combining automated and human methods, we investigate this issue in multiple dimensions quantificationally and conduct a set of evaluations on three prevailing T2I models (DALL-E v2, Stable Diffusion v1.5 and v2.1). Introducing attributes of cultural cluster and subject, we provide a fresh interdisciplinary perspective to bias analysis. The benchmark dataset UCOGC is presented, which encompasses authentic images of unique cultural objects from global clusters. Our results reveal that the culture of a disadvantaged country is prone to be neglected, some specified subjects often present a stereotype or a simple patchwork of elements, and over half of cultural objects are mispresented.

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.

This paper presents a comparative investigation of channel measurements and corresponding channel characteristics at 28 GHz, 38 GHz and 132 GHz in two different industrial environments, including a micro drilling-milling area and a large milling area. To depict the channel characteristics in industrial environments accurately, the close-in free space path-loss model and the alpha-beta-gamma model are used to examine path loss. Additionally, we analyze and compare the root mean square (RMS) delay spread (DS) and angular spread (AS) in different areas. The findings indicate the path loss exponent (PLE) has a dependency on the frequency, and all PLEs are less than the free space value 2.0 for the LoS case. Furthermore, RMS DS and AS exhibit distinct characteristics across various environments. This research provides valuable insights for design and optimization of industrial environmental systems.

Integrated Sensing and Communication (ISAC) has been considered a promising technology in the sixth generation (6G) system. An accurate channel model is a prerequisite for ISAC system design. This paper presents the first sensing and communication channel measurement campaign in typical indoor factory (inF) environments at 28 GHz and 38 GHz, in which over 720 spatial channel impulse responses are collected. The power-delay-angular profiles of multipath components are obtained, and the shared scatterers in communication and sensing channels are intuitively observed. A cluster centroid distance threshold-based cluster identification algorithm is novelly proposed to extract shared clusters from measured ISAC channels. Finally, the sharing proportional coefficient (SPC) is defined to measure the sharing feature of the ISAC channel. Results show that the channel SPC has an upward trend as the frequency increases. The observations presented in this work will offer promising support for ISAC wireless system evaluation.

The Terahertz (THz) band, which spans the frequency range from 0.1 to 10 THz, is widely regarded as a promising candidate for next-generation mobile communication. It has the potential to meet the demand for terabit-per-second data rates and facilitate network densification, making it a highly attractive field of research. In particular, the sub- THz band (100–300 GHz) is currently receiving significant attention in scientific research. This paper presents the characterization of Spatiotemporal channel measurements based on vector network analyzer in typical indoor scenarios at frequencies ranging from 215 to 225 GHz. The effects of both line-of-sight and non-line-of-sight conditions, caused by walls and pillars, are taken into consideration. Based on the directional measurements, the omnidirectional power angular delay profile is extracted. Char-acterization and analysis for multi path component parameters and cluster studies are also presented. The characterization of sub- THz channels extracted in this paper is instrumental in the development of channel modeling, system design, and performance assessment for sub- THz communication systems.

Orbital angular momentum (OAM) offers a novel and promising approach for resource reuse in wireless communication technology due to its theoretically infinite modality. This paper presents the large-scale fading characteristics of channel modeling. It comprehensively analyzes the path loss and Rician K-factor of OAM channels based on extensive measurements conducted at 30 GHz in multiple scenarios. Firstly, the paper introduces the traditional path loss fitting model. Building upon this, an innovative OAM model based on Laguerre-Gaussian (LG) beams is proposed, supported by actual measurements. Subsequently, the paper analyzes the absorption of OAM beams in different environments, including the lobby, office and corridor, utilizing measured data. It further characterizes the channel's distinct characteristics based on the observed performance in these diverse environments.

Terahertz (THz) communication technology holds significant potential for applications in the industrial internet of things (IIoT). Accurately characterizing the THz channel is critical for designing and optimizing communication systems in IIoT scenarios. However, the significantly higher frequencies in the THz band impede the effective utilization of channel models designed for microwave or millimeter-wave frequency bands. To overcome this challenge, extensive measurement campaigns are necessary to thoroughly investigate the characteristics of THz channels in indoor IIoT scenarios. This paper presents a measurement-based channel characterization in indoor IIoT scenarios at a frequency range of 215–225 GHz. We first present VNA-based channel measurement campaigns in micro drilling-milling and large milling areas. The measured data are further processed to obtain the channel impulse response. The key prop-agation channel parameters, e.g., path loss, power delay angle profile, delay spread, and angular spread, are calculated and analyzed in the line-of-sight case. Results demonstrate a favorable spatio-temporal consistency in multipath signal propagation and the physical spatial environment. Furthermore, significant correlations are observed between the channel characteristics and scatterer distribution within IIoT scenarios. The findings of this paper will make substantial contributions to the design and development of THz communication systems in IIoT scenarios.

Millimeter-wave (mmWave) and terahertz (THz) communication technologies have great application prospects in the Industrial Internet of Things (IIoT). However, the channels in industrial scenarios have not been fully investigated at multifrequency bands and in multiscenarios by using the same channel configurations, especially for the frequency ranges from mmWave to THz. In this article, channel measurements are conducted at 28, 38, 132, and 220 GHz in four industrial scenarios. The channel characteristics are extracted and modeled, including path loss, K factor, root mean square (RMS) delay spread, and angular spread (AS), and the correlation between K factor, RMS AS, and distance across the different frequencies in different scenarios. Specifically, we have extended the close-in path loss model to depict the excess loss caused by metal scatterers and modified AS model in the Third Generation Partnership Project (3GPP). Furthermore, cluster-level parameters at different frequencies are presented in our work. The results demonstrate that channel characteristics exhibit frequency dependence under the same measurement configuration. This study provides valuable guidance for the design and optimization of IIoT and contributes to the standardization process of 3GPP.

This paper presents the large-scale fading characteristics of Terahertz (THz) channel in indoor hotspot scenarios. A series of channel measurements at 219–224 GHz are conducted in a classroom and a hallway. In order to investigate the large-scale fading characteristics, the omnidirectional and best directional path loss are separately analyzed by close-in and floating-intercept models, and the Rician$K$-factor, root mean square delay spread and angular spread are analyzed to estimate the multi-path component richness, time and angle dispersion in various indoor scenarios. Further, these values are compared with what has been given in the Third Generation Partnership Project 38.901 in frequency bands lower than 100 GHz. In light of the results, the office area in hallway scenario shows the most severe path loss, and derives the highest time and angle dispersion. Besides, the measurement results enrich the datasets of THz channel propagation, which is helpful for the design and optimization of THz communication systems for sixth-generation.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection ( P d ).