Radar cross section (RCS) plays a critical role in modeling target scattering characteristics and enhancing the precision of target detection and localization in integrated sensing and communication (ISAC) systems. This paper investigates the human body RCS at 26 GHz via multi-angle channel measurements under different clothing conditions. Based on calibrated electromagnetic (EM) parameters, the RCS characteristics of the human body in far-field conditions are analyzed using ray-tracing (RT) simulations. Some suggestions for the design of ISAC systems are also discussed. The results provide a solid theoretical foundation and practical reference for the modeling of target scattering characteristics for ISAC channels.

This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks' unique architecture and routing issues and kernel stacks' inefficiency and development complexity. Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack. The proposed system uses technologies like Open vSwitch (OVS) and traffic control (TC) to emulate the space network's highly dynamic topology and time-varying link characteristics. The emulation results demonstrate the system's high reliability, and the user-space network stack reduces development complexity and debugging difficulty, providing convenience for the development of space network protocols and network functions.

Reliable channel data helps characterize the limitations and performance boundaries of communication technologies accurately. However, channel measurement is highly costly and time-consuming, and taking actual measurement as the only channel data source may reduce efficiency because of the constraints of high testing difficulty and limited data volume. Although existing standard channel models can generate channel data, their authenticity and diversity cannot be guaranteed. To address this, we use deep learning methods to learn the attributes of limited measured data and propose a generative model based on generative adversarial networks to rapidly synthesize data. A software simulation platform is also established to verify that the proposed model can generate data that are statistically similar to the measured data while maintaining necessary randomness. The proposed algorithm and platform can be applied to channel data enhancement and serve channel modeling and algorithm evaluation applications with urgent needs for data.

A novel digital twin (DT) enabled channel model for 6G vehicular communications in Beijing Central Business District (Beijing CBD) is proposed, which can support the design of intelligent transportation systems (ITSs). A DT space for Beijing CBD is constructed, and two typical transportation periods, i.e., peak and off-peak hours, are considered to characterize the vehicular communication channel better. Based on the constructed DT space, a DT-enabled vehicular communication dataset is developed, including light detection and ranging (LiDAR) point clouds, RGB images, and channel information. With the assistance of LiDAR point clouds and RGB images, the scatterer parameters, including number, distance, angle, power, and velocity, are analyzed under different transportation periods. The channel non-stationarity and consistency are mimicked in the proposed model. The key channel statistical properties are derived and simulated. Compared to ray-tracing (RT) results, the accuracy of the proposed model is verified.

The advent of 6G wireless networks promises unprecedented connectivity, supporting ultra-high data rates, low latency, and massive device connectivity. However, these ambitious goals introduce significant challenges, particularly in channel estimation due to complex and dynamic propagation environments. This paper explores the concept of channel knowledge maps (CKMs) as a solution to these challenges. CKMs enable environment-aware communications by providing location-specific channel information, reducing reliance on real-time pilot measurements. We categorize CKM construction techniques into measurement-based, model-based, and hybrid methods, and examine their key applications in integrated sensing and communication (ISAC) systems, beamforming, trajectory optimization of unmanned aerial vehicles (UAVs), base station (BS) placement, and resource allocation. Furthermore, we discuss open challenges and propose future research directions to enhance the robustness, accuracy, and scalability of CKM-based systems in the evolving 6G landscape.

As important infrastructure for airborne communication platforms, unmanned aerial vehicles (UAVs) are expected to become a key part of 6G wireless networks. Thus, modeling low- and medium-altitude propagation channels has attracted much attention. Air-to-ground (A2G) propagation channel models vary in different scenarios, requiring accurate models for designing and evaluating UAV communication links. Unlike terrestrial models, A2G channel models lack detailed investigation. Therefore, this paper provides an overview of existing A2G channel measurement campaigns, different types of A2G channel models for various environments, and future research directions for UAV air-land channel modeling. This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights non-suburban scenarios requiring consideration in future modeling efforts.

Recently, a novel type of neural networks, known as liquid neural networks (LNNs), has been designed from first principles to address robustness and interpretability challenges facing artificial intelligence (AI) solutions. The potential of LNNs in telecommunications is explored in this paper. First, we illustrate the mechanisms of LNNs and highlight their unique advantages over traditional networks. Then we explore the opportunities that LNNs bring to future wireless networks. Furthermore, we discuss the challenges and design directions for the implementation of LNNs. Finally, we summarize the performance of LNNs in two case studies.

In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities. In the early stage of video coding, in-loop filters, such as the deblocking filter, sample adaptive offset, and adaptive loop filter, were performed separately for each component. Recently, cross-component filters have been studied to improve chroma fidelity by exploiting correlations between the luma and chroma channels. This paper introduces the cross-component filters used in the state-of-the-art video coding standards, including the cross-component adaptive loop filter and cross-component sample adaptive offset. Cross-component filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values. We present their origin, development, and status in the current video coding standards. Finally, we conduct discussions on the further evolution of cross-component filters.

A monolithic integration of the light emitting diode (LED) and photodetector (PD) based on III-nitride is designed and fabricated on a sapphire substrate to act as a transceiver. Due to the coexistence of light emission and detection phenomenon of the multi-quantum well (MQW) structure, the monolithic transceiver can effectively sense environmental changes. By integrating a deformable Polydimethylsiloxane (PDMS) film on the transceiver chip, external force variation can be effectively detected. As the thickness of the PDMS reduces, the sensitivity significantly improves but at the expense of the measuring range. A sensitivity of 2.968 3% per newton for a range of 0–11 N is obtained when a 2 mm-thick PDMS film is packaged. The proposed monolithic GaN transceiver-based sensing system has the advantages of compactness, low cost, and simple assembly, providing an optional method for practical applications.

Cell-free networks can effectively reduce interference due to diversity gain. Two key technologies, access point (AP) clustering and transceiver design, play key roles in cell-free networks, and they are implemented at different layers of the air interface. To address the issues and obtain global optimal results, this paper proposes an uplink joint AP clustering and receiver optimization algorithm, where a cross-layer optimization model is built based on graph neural networks (GNNs) with low computational complexity. Experimental results show that the proposed algorithm can activate fewer APs for each user with a small performance loss compared with conventional algorithms.

To meet the demands of high-speed communication under strong electromagnetic interference, an all-light network (ALN) based on a multi-band optical communication system is proposed. It is designed for cross-scenario interconnection and networking, covering air, space, land, and sea. The ALN integrates four types of optical links: underwater blue light communication, white light illumination communication, solar-blind deep ultraviolet communication, and long-distance laser communication systems. These links are interconnected via Ethernet switches with the Transmission Control Protocol (TCP). Any ALN node supports both wired and wireless device access. The data transmission performance between network nodes was tested, with a maximum transmission delay of 73.3 ms, a maximum packet loss rate of 6.1%, and a maximum jitter of 15 ms. This comprehensive all-light network with all-scenario coverage lays the foundation for the future development of network technologies and the digital economy.