Channel state information (CSI) is essential to unlock the potential of reconfigurable intelligent surfaces (RISs) in wireless communication systems. Since massive RIS elements are typically implemented without baseband signal processing capabilities, limited CSI feedback is necessary when designing the reflection/refraction coefficients of the RIS. In this article, the unique RIS-assisted channel features, such as the RIS position-dependent channel fluctuation, the ultra-high dimensional sub-channel matrix, and the structured sparsity, are distilled from recent advances in limited feedback and used as guidelines for designing feedback schemes. We begin by illustrating the use cases and the corresponding challenges associated with RIS feedback. We then discuss how to leverage techniques such as channel customization, structured-sparsity, autoencoders, and others to reduce feedback overhead and complexity when devising feedback schemes. Finally, we identify potential research directions by considering the unresolved challenges, the new RIS architecture, and the integration with multi-modal information and artificial intelligence.

This paper proposes a novel blended hyper-cellular architecture for low-altitude aerial intelligent networks (LAINs) to provide agile coverage tailored to active air routes and takeoff/landing spots. Traditional cellular networks struggle to meet the dynamic demands of low-altitude UAV communications due to their rigid structures. The hyper-cellular network (HCN) architecture separates control and traffic coverage, enabling flexible and energy-efficient operations. The key components include control base stations (CBSs) for wide-area signaling coverage and traffic base stations (TBSs) that can be dynamically activated based on traffic demands. The proposed solution also integrates space information networks (SINs) to enhance the coverage efficiency. Key technologies such as all-G CBS using RISC-V architecture, AI-powered radio maps for low-altitude environments, and agile TBS coverage adaptation are introduced with some preliminary studies. These designs aim to address challenges like mobility management, interference coordination, and the need for real-time spectrum sharing in blended satellite-terrestrial networks. The proposed solution offers a scalable and agile framework to support the rapidly growing demand for reliable, low-latency, and high-capacity UAV communications in urban environments.

Ensuring end-to-end quality of service (QoS) for high-value services in satellite networks is challenging due to dynamic network topologies, varying QoS requirements, and the complex resource allocation across satellite beams and inter-satellite links. To this end, we propose a satellite traffic engineering framework with deterministic QoS (SatTED) by jointly optimizing resource allocation across access and bearer subnets.To tackle the complexity of joint scheduling, SatTED adopts a hierarchical logic-based benders decomposition (LBBD) architecture that coordinates access and bearer subnet resources. The master problem optimizes service admission and satellite selection via binary integer programming, while the subproblem handles routing and bandwidth allocation through linear programming relaxation. Key innovat-ions include scenario-cognizant Benders feasibility cuts to accelerate convergence and a critical constraint link preprocessing (CCLP) mechanism that reduces subproblem complexity by 5.15× in large-scale networks. In simulations on a 220-satellite network with 1 000 flows, SatTED improves total service payoff by 32% and increases high-value flow completion rates by 22%.

This paper centres on achieving the maximization of weighted throughput (WTP) in a multiuser cell-free massive multiple-input multiple-output (mMIMO) system with both finite blocklength (FBL) and infinite blocklength (INFBL), which is conducted against the backdrop of constrained time-frequency resources. We aim to ensure quality of service (QoS) for all users, particularly in the FBL scenario, maintaining an acceptable latency and block error rate (BLER). To counteract the impact of reduced DoF of channel matrix due to a large number of users accessing the system, which leads to decreased system performance, we strive to optimize WTP by scheduling multiple users to different resource elements (REs) and applying precoding operations accordingly, subject to the limitations imposed by total power consumption per time slot and requisite QoS parameters. Simulation results demonstrate the superiority of the proposed multiuser processing (MUP) scheme over both single-user processing (SUP) and all-user processing (AUP) alternatives, and the proposed iterative algorithm based on genetic algorithm (GA) achieves up to $49.36\%$ system performance gains compared to the benchmark algorithms. This substantiates the efficacy of our method in enhancing network performance and user satisfaction.

For large-scale multiple-input multiple-output (MIMO) systems, iterative detection algorithms based on belief propagation (BP) have shown near-optimal performance. However, the existing deterministic implementations of BP detection are considerably complex, particularly for MIMO systems with a large scale. %This paper proposes an alternative approach using stochastic computation, offering advantages in both hardware complexity and fault tolerance. This paper proposes an alternative approach using stochastic computation.} This paper introduces a hardware architecture for stochastic message updating of observation nodes and symbol nodes. The signed stochastic real division (SRD) and the stochastic real addition (SRA) are proposed for latency consideration, and multi-bit designs are proposed to improve the performance and reduce the stream length. The look up table (LUT) has been modified for serial input to enhance hardware efficiency. Simulation results demonstrate that for large-scale MIMO systems with either QPSK or 16-QAM, the stochastic BP detector achieves similar performance as the deterministic one. To highlight its implementation advantage, an $8\times 32$ stochastic MIMO detector with QPSK is implemented. Results show that its hardware efficiency is about 10 times greater than existing works and has lower complexity compared with deterministic designs.

Congested link detection (CLD) has attracted more and more attention due to the rapid development of data traffic and services. In this work, we proposed a novel compressive sensing (CS) aided deep learning CLD scheme. Firstly, considering the network tomography structure, we proposed a CS-based preliminary CLD process to estimate the congestion probabilities for each link by utilizing the sparsity of congestions, it enables a decrease in the number of monitors needed, which in turn, enhances the flexibility and practicality of our scheme in various real-world scenarios. Then, based on the CS aided preliminary estimation, a long short-term memory network (LSTMN) is exploited to extract the time relationship of the congested states for each link, which can improve the accuracy of CLD. Moreover, since LSTMN utilizes the CS-aided preliminary estimation results to extract time relationships, our proposed scheme can reduce the monitoring cost and improve CLD accuracy. Ultimately, the simulation results substantiate the efficacy of our proposed scheme.

In this paper, a method for designing super-massive sparse phased arrays (SMSPAs) known as the unitary modified matrix enhancement and matrix pencil (UMMEMP) method is proposed. In this method, an eigenvalue pairing method, which is inspired by the modified MEMP, effectively pairs the repeated eigenvalues intractable in the unitary matrix pencil method, and it is more effective in determining the locations of elements in the sparse array. Three numerical examples and a full-wave validation are presented to demonstrate the effectiveness of the method, implemented via SMSPA, in achieving low sidelobe level wide-angle scanning radiation patterns, circular flat-top radiation patterns, and ultra wide-angle scanning radiation patterns.

The rapid development of the low-altitude economy has imposed unprecedented demands on wireless infrastructure to accommodate large-scale drone deployments and facilitate intelligent services in dynamic airspace environments. However, unlocking its full potential in practical applications presents significant challenges. Traditional aerial systems predominantly focus on air-ground communication services, often neglecting the integration of sensing, computation, control, and energy-delivering functions, which hinders the ability to meet diverse mission-critical demands. Besides, the absence of systematic low-altitude airspace planning and management exacerbates issues regarding dynamic interference in three-dimensional space, coverage instability, and scalability. To overcome these challenges, a comprehensive framework, termed low-altitude wireless network (LAWN), has emerged to seamlessly integrate communication, sensing, computation, control, and air traffic management into a unified design. This article provides a comprehensive overview of LAWN systems, introducing LAWN system fundamentals and performance evaluation metrics. Subsequently, we delve into the evolution of functional designs and review critical concerns surrounding privacy and security in the open-air network environment. We survey advanced artificial intelligence techniques that enhance LAWN functionality and enable increasingly autonomous operations. Finally, we present the cutting-edge developments in airspace structuring, air traffic management, and path planning, providing insights to facilitate the practical deployment of LAWNs.

Meteor-burst communications (MBC) use ionized meteor trails to enable long-distance, beyond-line-of-sight links. Most existing models address long-range forward-scatter configurations, whereas back-scatter scenarios for short-range communications are less well studied. This paper presents a statistical, comparative analysis of meteor-trail parameters using observations from two back-scatter meteor radars operating at different frequencies (53.1 MHz at Qujing, 25.6°N, 103.8°E; and 39.0 MHz at Lincang, 23.63°N, 99.83°E) in Yunnan, Southwest China. The analysis is based on data collected over a 46-day period from 13th December 2024 to 27th January 2025, during which the 39.0 MHz system detected approximately 360 000 meteor events and the 53.1 MHz system detected approximately 280 000 events. We focus on spatiotemporal characteristics, decay times, and inter-arrival times of meteor trails. Detection rates exhibit strong diurnal variation, with higher counts in the local morning. Operating frequency strongly affects the detection of specular height: the peak of the meteor specular height distribution occurs near 85 km for the 53.1 MHz system and near 88 km for the 39.0 MHz system. While the lower-frequency radar generally provides higher echo occurrence rate and shorter inter-arrival times, this advantage reverses during local afternoon hours, when the higher-frequency system achieves a higher echo occurrence rate coupled with shorter inter-arrival times. These results provide quantitative constraints and prior information useful for optimizing short-range MBC systems, particularly with respect to frequency selection, temporal scheduling, and antenna configuration.

In this paper, we address the visible region (VR) detection problem in ultra-large-scale (ULS) uniform planar array (UPA) reconfigurable intelligent surface (RIS)-assisted communication systems. We propose a practical VR detection method for each individual RIS element even without channel state information (CSI). Furthermore, we introduce an adaptive binary search method that leverages the block characteristics of obstructed region (OR) in two different scenarios: the rectangular OR and the irregular OR. Numerical results demonstrate that the proposed method significantly reduces the detection overhead while achieving a detection accuracy comparable to, or even surpassing, that of the individual detection method. It can achieve accurate VR detection under sufficient transmitting power, even when the CSI is completely unavailable. % This work ensures precise VR detection in uniform planar array (UPA)-configured RIS systems, which is crucial for subsequent channel estimation and UE localization, ultimately maximizing the performance benefits of RIS deployment.

High-performance positioning, navigation and timing (PNT) service is critical to the safe flight of low-altitude aircraft and the effective management of low altitude traffic. In low-altitude economic scenarios, the specificity of massive unmanned aerial vehicle (UAV) flights and the complexity of low-altitude airspace traffic management impose stringent demand on the high-continuity, high-accuracy, real-time, and high-security PNT service. However, the current PNT service, which primarily relies on Global Navigation Satellite System (GNSS), Micro-Electro-Mechanical System Inertial Navigation System (MEMS INS), etc., is completely inadequate to support the future needs of low-altitude economic development. In order to bridge the huge gap between existing capability and future demand, a three-layer PNT architecture based on the collaboration of space-based, air-based and ground-based PNT systems is proposed for low-altitude economy. The space-based layer consists of high, medium even possible low orbit GNSS constellations, such as BeiDou Navigation Satellite System (BDS), for high-precision, high-security absolute positioning and timing. The air-based layer leverages inter-aircraft links for high-reliability dynamic relative positioning. The ground-based layer includes pseudolite network, as well as 5G-advanced (5G-A)/6G network, for more comprehensive coverage and real-time positioning. To this end, it is imperative to make breakthroughs in key technologies, from systems to airborne terminal, including but not limited to high-precision anti-jamming GNSS signal processing, high-reliability relative positioning, real-time pseudolite positioning, and high-efficient multi-source information fusion at airborne terminal, etc. Due to the moderate redundancy, heterogeneous mechanism, and multiple coverage from multiple PNT systems, the proposed layered PNT architecture possesses high robustness and resilient. Additionally, the integration of INS, LiDAR and vision etc. perception technologies can significantly enhance the PNT capability. As a result, the proposed three-layer PNT architecture enable greater autonomy for low-altitude aircraft and intelligent traffic management for massive UAV operations, and promoting the safe and efficient development of the low-altitude economy.

Modulation signals are likely to experience channel fading when transmitted over wireless channels, and channel blind equalization is a powerful method to combat fading and guarantee high transmission quality and efficiency because no pilot information is required. In this paper, we propose a dual-branch blind equalization autoencoder with hybrid attention mechanisms, referred to as DBeA-HA, to restore various modulation signal waveforms. In the main branch, we employ multi-layer depthwise separable convolutions (DSC) with rich residual connections and squeeze-and-excitation (SE) mechanism in our encoder to extract channel features of faded signals at different resolutions, while reducing complexity. The auxiliary branch is constructed by incorporating a lightweight residual temporal dilated convolution to capture temporal correlations of faded signals. Additionally, the convolutional block attention module (CBAM) and multi-head self-attention (MHSA) mechanisms are applied within and between branches, respectively, to further enhance feature extraction and fusion capabilities. By integrating two branches effectively, the proposed method achieves high equalization performance and keeps low complexity. Experimental results reveal that in signal-to-noise ratio (SNR) ranging from -12-8 dB with six-path fading, our DBeA-HA effectively compensates for the effects of fading channels and surpasses existing equalization methods. For demodulation, compared with “ResNet+De” and the least mean square (LMS) methods, our DBeA-HA reduces the BER of QPSK by an average of 32.66% and 72.98%, and reduces the BER of 16-QAM by an average of 19.41% and 55.33%, respectively, with a moderate level of complexity.

Space laser communication (SLC) is an emerging technology to support high-throughput data transmissions in space networks. In this paper, to guarantee the reliability of high-speed SLC links, we aim at practical implementation of low-density parity-check (LDPC) decoding under resource-restricted space platforms. Particularly, due to the supply restriction and cost issues of high-speed on-board devices such as analog-to-digital converters (ADCs), the input of LDPC decoding will be usually constrained by hard-decision channel output. To tackle this challenge, density-evolution-based theoretical analysis is firstly performed to identify the cause of performance degradation in the conventional binary-initialized iterative decoding (BIID) algorithm. Then, a computation-efficient decoding algorithm named multiary-initialized iterative decoding with early termination (MIID-ET) is proposed, which improves the error-correcting performance and computation efficiency by using a reliability-based initialization method and a threshold-based decoding termination rule. Finally, numerical simulations are conducted on example codes of rates 7/8 and 1/2 to evaluate the performance of different LDPC decoding algorithms, where the proposed MIID-ET outperforms the BIID with a coding gain of 0.38 dB and variable node calculation saving of 37%. With this advantage, the proposed MIID-ET can notably reduce LDPC decoder's hardware implementation complexity under the same bit error rate performance, which successfully doubles the total throughput to 10 Gbps on a single-chip FPGA.

As the development of 6G accelerates, non-terrestrial networks (NTN) are emerging as a critical component to ensure seamless global coverage communication. This paper systematically reviews the evolution of the third-generation partnership project (3GPP) NTN standardization, from initial discussions in Release 15 to the in-depth optimizations in Release 19 and beyond. Through a detailed gap analysis, we identify key technical and industrial challenges across the wireless, network, and terminal domains that hinder the realization of a fully integrated space-terrestrial network. To address these challenges, we propose a 6G-oriented holistic architecture composed of three segments and three functional layers. We further outline essential enabling technologies, including networking technologies and intelligent resource management technologies. To validate the feasibility and effectiveness of the proposed architecture, we present a case study on integrated sensing and communication in high-speed mobility scenarios. Simulation results demonstrate significant performance gains in robustness, sensing accuracy, and adaptability compared to conventional approaches. Our findings establish a solid foundation for future research and standardization of 6G integrated networks, aiming to achieve intelligent, ubiquitous, and resilient communication infrastructures across space, air, and ground domains.

(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle (UAV) air-to-ground channels are derived for the first time using a novel spatial-vector-based method from a three-dimensional (3-D) arbitrary-elevation one-cylinder model. The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density, the level crossing rate, and the average fading duration, which are shown to be the generalizations of those previously obtained from the two-dimensional (2-D) one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels. The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics. Based on the derived expressions, the impacts of some parameters on the channel characteristics are investigated in an effective, efficient, and explicable way, which leads to a general guideline on the manual parameter estimation from the measurement description.

Due to the advantages of high mobility and line-of-sight transmission, unmanned aerial vehicles (UAVs) equipped with mobile edge computing (MEC) servers can effectively reduce the computational burden and task delay of ground users (GUs). However, the offloading data from GU to UAV is vulnerable to be eavesdropped by malicious users in the network. Thus, this paper proposes a secure cooperative offloading scheme in a multi-UAV-assisted MEC network, where each UAV has the capability to partially distributing the tasks to other idle UAVs. Specifically, we first model the task offloading decision process of GUs based on the multi-agent Markov Decision Process (MDP) framework. Then we optimize the offloading decision of GUs by adopting multi-agent deep determined policy gradient (MADDPG) to minimize the overall system latency for task processing and computation offloading. Simulation results verify that the proposed cooperative offloading scheme can effectively reduce the system latency compared with the benchmark.

Space-terrestrial integrated networks (ST-IN) require a trustworthy and auditable environment for multi-party spectrum sharing under dynamic and heterogeneous conditions. Blockchain, as a decentralized ledger, exhibits promising properties such as transparency and tamper resistance, making it a potential enabler for such scenarios. However, conventional blockchain-based approaches tightly couple strategy execution with transaction consensus, resulting in excessive overhead and poor adaptability to fast-changing spectrum semantics. To address these issues, this paper presents a spectrum-semantics-driven meta-consensus framework built upon a directed acyclic graph (DAG) mainchain architecture. By decoupling policy optimization from on-chain coordination and leveraging semantic representations of spectrum states for meta-level consensus and policy migration, the framework enables agile and scalable spectrum sharing across dynamically clustered network agents. Simulation results verify that the proposed design significantly enhances spectrum utilization and adaptability while maintaining decentralized transparency and auditability in large-scale STIN environments.

In this paper, we propose a robust coherent joint transmission scheme for user-centric cell-free orbital angular momentum (OAM) networks. We formulate a coherent joint transmission (JT) problem to maximize the weighted sum capacity. The formulation enforces per-access point (AP) power budgets and per-mode exclusivity. To address the coupling between mode selection and beamforming, a lightweight block coordinate descent based fractional programming (BCD-FP) algorithm is developed, which alternates between mode assignment, AP scheduling, and beamforming. Extensive simulations validate that the proposed method improves sum capacity by approximately 1.4 to 1.9 times over stochastic gradient descent (SGD) and greedy baselines, reaching 85-90% of the perfect channel bound. Results also indicate an additional 8-10% gain over non-OAM counterparts. Furthermore, simulations under high mobility demonstrate the algorithm's robustness against channel aging, validating its efficacy in dynamic environments.

Mobile edge computing (MEC) has been envisioned as an important technique of the 5th generation (5G) wireless networks to handle computation tasks from mobile terminal devices (TDs). To accommodate the low-delay requirements of mobile TDs with latency-sensitive and computation-intensive applications, we propose an edge-edge collaborative paradigm for software-defined networking (SDN)-enabled MEC networks by merging adjacent MEC servers with redundant computing resources to execute offloading tasks. Meanwhile, since task data distributed among various edge computation nodes is vulnerable to malicious attacks and eavesdropping, resulting in data and privacy leakage. We take secure offloading service into account and attempt to minimize the total cost of all offloading tasks by the optimal scheme, jointly considering offloading decision, security level and computing resource assignment. To reduce the total cost of all offloading tasks mainly caused by wireless communication, energy consumption and secure computing, we propose an optimization framework which can dynamically adjust the task offloading decision, security level and computing resource allocation policy simultaneously in MEC networks. We formalize the problem as a multi-agent decision based optimization problem, and further address it by using a multi-agent deep reinforcement learning (DRL) based method. Numerical results illustrate that the proposed DRL-based method is superior to the benchmark methods in terms of the total cost and support ratio.

With the rapid growth of the low-altitude economy, the demand for typical low-altitude applications has accelerated the advancement of integrated sensing and communications (ISAC) networks. This paper begins by analyzing representative application scenarios to clarify the core requirements of the low-altitude economy for modern ISAC networks. By investigating the distinctive characteristics of ISAC networks in low-altitude environments, it presents a comprehensive analysis of key challenges and identifies four major issues: challenges in precise target detection, interference management, inconsistent sensing and communication coverage, and the complexity of air-ground coordination and handover. Based on fundamental theories and principles, the paper proposes corresponding solutions, encompassing advanced technologies for precise target detection and recognition, high-reliability networked detection, robust interference management, and seamless air-ground collaboration. These solutions aim to establish a solid foundation for the future development of intelligent low-altitude networks and ensure effective support for emerging applications.

In hybrid beamforming design using the conventional gradient projection (GP) algorithm, it is common to use a fixed step size, which results in a slow convergence rate and unsatisfactory achievable rate performance. This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem. The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique, and every step in deep learning is explainable. Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm.

Utilizing unmanned aerial vehicles (UAVs) for flexible and efficient mobile data dissemination offers a promising solution for infrastructure-less internet of things applications. This paper investigates a UAV-enabled data dissemination system in which a multiantenna UAV is deployed to disseminate data to ground terminals (GTs). Under the constraints of UAV speed, communication outage probability (OP), UAV transmission power, and task data size, we aim to minimize the system mission completion time (MCT) by optimizing the UAV trajectory, UAV-GT transmission scheduling, and bandwidth allocation, while considering the imperfect channel state information (CSI). To address this challenging problem, we introduce matched-filter precoding and orthogonal frequency division multiplexing (OFDM) techniques and propose a two-stage optimization algorithm. Initially, an offline optimization algorithm is developed to determine the optimum UAV trajectory and the MCT. Subsequently, an online optimization algorithm is proposed to enhance the subchannel and transmission power allocation. Finally, the numerical results confirm the effectiveness of the proposed scheme, demonstrating a substantial improvement in the time efficiency of UAV data dissemination.

In this paper, we proposed a novel millimeter wave channel emulation platform for satellite communication scenarios that can support Q/V frequency bands with a maximum bandwidth of 1 GHz. The platform has been calibrated for frequency flatness and frequency offset, and its reliability has been demonstrated through closed-loop verification based on standard testing instruments. At the same time, a method for modeling and analyzing satellite channels and real-time emulation environment was constructed, which can model and analyze satellite channels in different application scenarios. The obtained channel parameters are injected into the channel emulation platform to achieve real-time emulation of satellite channels in various application scenarios (stationary, vehicle mounted, shipborne, airborne, etc.). Finally, we conducted performance verification on the constructed satellite channel hardware-in-the-loop simulation system. This platform can provide support for key technology research and equipment performance evaluation in satellite communication.

Given the rapid advancements in wireless communication and terminal devices, high-speed and convenient Wi-Fi has permeated various aspects of people's lives, and attention has been drawn to the location services that Wi-Fi can provide. Fingerprint-based methods, as an excellent approach for localization, have gradually become a hot research topic. However, in practical localization, fingerprint features of traditional methods suffer from low reliability and lacking robustness in complex indoor environments. To address the identified shortcomings, this paper introduces Secci, an innovative feature-enhanced intelligent localization system underpinned by a squeeze-and-excitation convolutional neural network (SE-CNN) model, utilizing a rich array of channel state information (CSI) data. By modifying the device driver, diversified CSI data are extracted and converted into red, green, and blue (RGB) CSI images, which serve as input to the SE-CNN training in the offline stage, leveraging the SE network's attention mechanism for improved performance. Employing a greedy probabilistic approach, rapid prediction of the estimated location is performed in the online stage using test CSI images. The Secci system is implemented using off-the-shelf Wi-Fi devices, and comprehensive experiments are carried out in two representative indoor environments to showcase the superior performance of Secci compared to four existing algorithms.

In this paper, we investigate the secure communication in internet-of-things (IoT) network. Specifically, we propose a novel dual-IRS-UAV assisted secure transmission scheme, wherein two unmanned aerial vehicles (UAVs), each carriying an intelligent reflecting surface (IRS), can adjust their horizontal positions to efficiently assist the secure transmission from the IoT data sender to the receivers. We aim to maximize the minimum system secrecy rate by jointly optimizing the horizontal locations of UAVs, the transmit beamforming of IoT data sender and the reflection phase shifts of IRSs. The considered problem is non-convex. We first divide it into several sub-problems which are then transformed using the first-order Taylor expansion approach, which can be solved by standard optimization tools. Numerical results show the performance gain in minimum system secrecy rate achieved by the proposed dual-IRS-UAV assisted secure transmission scheme over the benchmark schemes.

This work presents a concept of a multiple-input multiple-output (MIMO) antenna with wideband, high isolation, and high gain characteristics. Wideband performance is achieved using a patch antenna with a substrate suspended from an air gap. Unlike the conventional MIMO designs, in which only one element is excited for each port, the proposed antenna excites two MIMO elements simultaneously with the aid of a wideband hybrid coupler. Consequently, high gain radiation without requiring additional MIMO element and high isolation can be simultaneously achieved. A prototype designing at the central frequency of 2.45 GHz has been fabricated and measured, showing an impedance bandwidth of 25.7% (2.11--2.74~GHz) with a minimum isolation between two ports of 15 dB, and a peak gain of about 10.35 dBi.

Communications system has a significant impact on both operational safety and logistical efficiency within low-altitude drone logistics networks. Aiming at providing a systematic investigation of real-world communication requirements and challenges encountered in Meituan UAV's daily operations, this article first introduces the operational scenarios within current drone logistics networks and analyzes the related communication requirements. Then, the current communication solution and its inherent bottlenecks are elaborated. Finally, this paper explores emerging technologies and examines their application prospects in drone logistics networks.

Full-duplex (FD) integrated access and backhaul (IAB) system requires effective self-interference (SI) suppression, relying on accurate channel estimation with high pilot overheads. This paper proposes a cost-effective end-to-end (E2E) hybrid beamforming framework for FD enabled millimeter-wave (mmWave) IAB systems with implicit channel state information (CSI), which unifies the pilot training and data communication to avoid accurate channel estimation. We introduce a Sensing Transformer block to sense spatial channels of the network, where sensing vectors are designed for pilot observation. Specifically, the Sensing Transformer is designed to implement an attention mechanism to effectively leverage implicit CSI from sequential pilot observations with an arbitrary length. Thereafter, we combine graph neural networks and Transformer mechanisms as a novel Graphformer algorithm to design hybrid beamformers with high spectrum efficiency, which can efficiently mitigate multi-user interference and SI via capturing both local and global spatial structure features of the whole network. Numerical results verify that: 1) The proposed E2E framework can significantly improve the spectrum efficiency with lower pilot overheads compared with the conventional method with explicit channel estimation. 2) The proposed E2E framework exhibits superior channel sensing and interference cancellation performance over traditional learning-based schemes across various network configurations, even with modest pilot overheads.

With the rise of the internet of things demanding low latency, the significance of real-time transmission communications is being increasingly emphasized. In real-time transmission systems, there are two ways to do the transmission: without and with channel coding, and therefore, it is worth comparing the performance of these ways. In this paper, the distortions of these two systems are derived. Specifically, a general formula for calculating distortion in wireless communication systems is first provided. Next, variations of the formula are developed for three standard wireless communication systems: single-input, single-output (SISO); multiple-input, multiple-output (MIMO) with a Gaussian channel; and mmWave MIMO. Theoretical derivations, supported by simulation results, demonstrate that systems without channel coding exhibit less distortion than those with channel coding on average.

Cognitive radio enables efficient utilization of spectrum resources and adapts to changing environments through intelligent sensing and adaptability. However, centralized spectrum allocation limits their efficiency. This paper proposes a blockchain-based priority auction consensus mechanism for dynamic spectrum allocation in cognitive radio. The distributed spectrum allocation improves efficiency, the use of blockchain ensures transparent and secure transactions, while the priority auction consensus ensures the communication quality of users. Simulation results validate that primary users achieve near-optimal performance, while secondary and other users guarantee communication performance, resulting in an overall spectrum utilization close to 100%. These highlight the potential of the priority auction consensus mechanism in enhancing the spectrum management of cognitive radio networks.

Orthogonal time frequency space (OTFS) has been broadly studied and applied in various fields due to its robustness in high dynamic scenes. In this paper, we investigate the application of OTFS in radar-embedded communication systems, and propose an OTFS-based radar-embedded communication model suitable for high-speed mobile scenarios. By comparing the communication reliability at different velocities, the benefits of OTFS-based radar-embedded communication systems over conventional linear frequency modulation (LFM) based systems are verified. The proposed algorithm also shows excellent radar detection performance, and compensates the channel state information according to the motion parameters of the target to achieve more accurate communication reception. Besides that, our algorithm exhibits superior covertness performance to that of LFM.

In the process of minimizing the training loss of machine learning model pursuit, it is very easy to inadvertently remember sensitive private data, which leads to data reconstruction, member reasoning attacks and other security problems. In order to mitigate these risks, differentiated privacy has become a key standard for privacy preserving machine learning. The classical differential privacy depth learning algorithm, differential privacy stochastic gradient descent (DP-SGD), that adapts the standard stochastic gradient descent (SGD) algorithm to incorporate differential privacy, ensuring that the trained model doesn't reveal sensitive information about individual training data points. However, DP-SGD has the problems of slow convergence speed and large utility loss. We propose an effective solution that is the cooperative combination of selective updating and early vertical gradient disturbance. Selective updating ensures that the training track of the model is aligned with the optimal direction, significantly accelerating convergence. Subsequently, the application of vertical gradient perturbation ensures that the model with significantly improved accuracy can be achieved even under strict privacy constraints (small privacy budget). Through theoretical analysis and a large number of experiments, this paper proves that DP-VGPSU has superior performance in convergence speed and accuracy.

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) technology to achieve parallel access for multiple users. However, when a large number of users access, the probability of collision will be greatly increased, resulting in greatly reduced throughput. In high-density wireless networks, in order to reduce collisions between users, a hybrid contention/designation-based uplink OFDMA random access (HCDRA) scheme is proposed. The proposed scheme adopts a hybrid of contention and transmission of designation, where the access point (AP) arranges one of the collided stations (STA) on the resource unit (RU) for transmission of designation in the next transmission, and other STAs still follow the traditional contention mechanism. The addition of designated transmission scheme can effectively reduce the intensity of contention and improve the utilization rate of RU without knowing the prior STA and without increasing the overhead of the sensing time. The proposed scheme can greatly improve throughput and latency performance without introducing additional overhead. Moreover, our proposed hybrid scheme has almost no changes to the IEEE 802.11ax standard protocol, and is suitable for IEEE 802.11ax and beyond. The feasibility of the proposed scheme has been demonstrated through numerical simulation results.

Due to the elimination of cell boundaries, cell-free massive multiple-input multiple-output (MIMO) facilitates seamless handover and ensures consistent service, making it suitable for high-speed railway communications. However, high mobility causes rapid variations in the train-to-ground wireless channel, leading to significant channel aging, which severely undermines channel state information accuracy and degrades system performance, especially in cell-free setups where channel hardening is limited. In this paper, the impact of channel aging caused by high-speed train movement and the performance gains achievable through downlink training are investigated. We derive the downlink spectral efficiency (SE) and compare the outcomes for cases with and without downlink training for cell-free massive MIMO systems. Given the limited coherence time, an algorithm is proposed to mitigate the overhead from downlink training, which enables users to select distributed access points based on large-scale fading coefficients. Orthogonal pilot sequences are allocated to users associated with the same access point, effectively lowering pilot overhead and mitigating pilot contamination. Simulation results demonstrate that the benefits of downlink training offset the performance loss due to limited data transmission time, thereby enhancing system SE. Moreover, in situations characterized by elevated user density, the proposed pilot assignment algorithm markedly improves the system's total SE.

Based on current research, there is a lack of comprehensive review articles that systematically address the security issues of the internet of vehicles (IoV) using blockchain technology. In this study, we thoroughly analyze the threats and challenges within IoV systems and provide a systematic review of blockchain-based IoV security solutions. This paper summarizes blockchain solutions for addressing key challenges in IoV, including network security, communication efficiency, resource optimization, data management, and transaction operational efficiency. Our findings indicate that blockchain technology can enhance V2X communication security, optimize resource utilization, ensure data integrity, and improve transaction security. Additionally, this paper explores future research directions, including advanced security mechanisms, efficient consensus algorithms, the integration of edge computing, and intelligent applications, demonstrating the potential of blockchain in tackling IoV system challenges.

Large language models (LLMs) are rapidly transforming the design and operation of communication systems, while the advent of 6G networks provides the infrastructure necessary to sustain their unprecedented scale. This survey investigates the bidirectional relationship between LLMs and 6G networks from two complementary perspectives. From the perspective of LLM for Network, we illustrate how LLMs can enhance network management, strengthen security, optimize resource allocation, and act as intelligent agents. By leveraging their natural language understanding and reasoning capabilities, LLMs offer new opportunities for intent-driven orchestration, anomaly detection, and adaptive optimization beyond the scope of conventional AI models. From the Network for LLM perspective, we discuss how 6G-native features support scalable, efficient, and sustainable LLM training and inference across the edge and cloud. Building on these two perspectives, we identify key challenges related to scalability and efficiency, robustness and security, as well as trustworthiness and sustainability. We further highlight open research directions as well. We envision that this work serves as a roadmap for cross-disciplinary research, fostering the integration of LLMs and 6G toward trustworthy and intelligent next-generation communication systems.

This paper presents an 8-port multiple-input multiple-output (MIMO) array with single-layer and compact size characteristics for 5G internet of things (IoT) devices. The proposed array employs four dual-polarized radiators arranged in a $2 \times 2$ configuration to realize 8 uncorrelated streams. Each radiator is formed by combining a crossed patch with four parasitic elements for bandwidth enhancement. For mutual coupling mitigation, multiple shorting pins are inserted between the radiators. Accordingly, high isolation can be obtained without requiring large spacing between the MIMO elements. The final array has overall dimensions of $57.2 \text{ mm} \times 57.2 \text{ mm} \times 1.6 \text{ mm}$, or about $0.82\lambda \times 0.82\lambda \times 0.02\lambda$ at the lower edge frequency of the operating band from $4.32$ to $4.68 \text{ GHz}$, corresponding to about 8%. Across this band, the inter-port isolations are always better than $17 \text{ dB}$.

The low Earth orbit (LEO) satellite networks play an important role in the future communication networks. However, under the end-to-end (E2E) transmission background, inter-satellite routing has been widely studied, but the influence of ground-satellite links (GSL) on routing has received less attention. In this paper, a fast E2E satellite routing algorithm based on access node selection is proposed. Firstly, the delay of four path modes generated by users accessing the network from different satellites is analyzed, and the influence of delay on E2E routing performance is presented. Then, jointly considering routing delay and node load, an access node selection strategy is proposed by using the shortest E2E delay to determine the access source and destination node within satellites. Finally, an optimization domain is divided from the network topology by using the shortest delay path based on hops constraints. And a routing optimization algorithm based on Q-learning has been proposed in the optimization domain, realizing high computational speed and stable results. The simulation results show that the access node selection strategy can decrease E2E delay by up to $10$~ms and enhance the performance of node load balancing. And the routing optimization algorithm can reduce the average computation time.

Spectrum map construction, which is crucial in cognitive radio (CR) system, visualizes the invisible space of the electromagnetic spectrum for spectrum-resource management and allocation. Traditional reconstruction methods are generally for two-dimensional (2D) spectrum map and driven by abundant sampling data. In this paper, we propose a data-model-knowledge-driven reconstruction scheme to construct the three-dimensional (3D) spectrum map under multi-radiation source scenarios. We firstly design a maximum and minimum path loss difference (MMPLD) clustering algorithm to detect the number of radiation sources in a 3D space. Then, we develop a joint location-power estimation method based on the heuristic population evolutionary optimization algorithm. Considering the variation of electromagnetic environment, we self-learn the path loss (PL) model based on the sampling data. Finally, the 3D spectrum is reconstructed according to the self-learned PL model and the extracted knowledge of radiation sources. Simulations show that the proposed 3D spectrum map reconstruction scheme not only has splendid adaptability to the environment, but also achieves high spectrum construction accuracy even when the sampling rate is very low.

Meteor burst communication exploits transient meteor trails to enable beyond-line-of-sight transmission, where the inherently short-lived channel conditions impose stringent requirements on rapid and reliable signal-to-noise ratio estimation. This study proposes a novel pilotless SNR estimator founded on the two-sample order statistics method, which quantifies the distributional deviation between received signal amplitudes and pre-generated reference distributions without assuming a specific noise model. Comprehensive Monte Carlo simulations under BPSK, QPSK, and 16-QAM modulations in additive white Gaussian noise environments demonstrate that the proposed OS-based approach consistently surpasses the M2M4, M8, and Kolmogorov-Smirnov estimators in terms of estimation accuracy, normalized mean squared error, and success rate, with particularly notable gains in limited-sample scenarios. These results underscore the method’s robustness and adaptability for SNR estimation in MBC applications.