(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.

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.

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.

In this paper, we analyze the physical layer security (PLS) performance of a free-space optical (FSO) communication system composed of a transmitting satellite and ground users. Specifically, the FSO fading channels follow the Málaga distribution. Further, we scrutinize the influence of non-zero boresight pointing errors and angle-of-arrival fluctuations on the PLS performance for the first time. We derived the probability density function and cumulative density function of the FSO link, followed by the closed-form expressions of the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC). The asymptotic SOP expression at the high signal-to-noise ratio regime and diversity order are also provided to reveal the physical mechanism of the PLS of the considered system. Finally, Monte Carlo simulation results are presented to verify the correctness of the analytical expressions. The results afford helpful insights for the future design of satellite FSO communication systems.

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.

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.

With more and more IoT terminals being deployed in various power grid business scenarios, terminal reliability has become a practical challenge that threatens the current security protection architecture. Most IoT terminals have security risks and vulnerabilities, and limited resources make it impossible to deploy costly security protection methods on the terminal. In order to cope with these problems, this paper proposes a lightweight trust evaluation model TCL, which combines three network models, TCN, CNN, and LSTM, with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device, and the trust evaluation of the terminal's continuous behavior can be achieved by combining the scores of different periods. After experiments, it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763, 94.456, 99.923, and 99.195 on HDFS, BGL, N-BaIoT, and KDD99 datasets, respectively, and the size of TCL is only 91KB, which can achieve similar or better performance than CNN-LSTM, RobustLog and other methods with less computational resources and storage space.

This paper proposes Flex-QUIC, an AI-empowered quick UDP Internet connections (QUIC) enhancement framework that addresses the challenge of degraded transmission efficiency caused by the static parameterization of acknowledgment (ACK) mechanisms, loss detection, and forward error correction (FEC) in dynamic wireless networks. Unlike the standard QUIC protocol, Flex-QUIC systematically integrates machine learning across three critical modules to achieve high-efficiency operation. First, a contextual multi-armed bandit-based ACK adaptation mechanism optimizes the ACK ratio to reduce wireless channel contention. Second, the adaptive loss detection module utilizes a long short-term memory (LSTM) model to predict the reordering displacement for optimizing the packet reordering tolerance. Third, the FEC transmission scheme jointly adjusts the redundancy level based on the LSTM-predicted loss rate and congestion window state. Extensive evaluations across Wi-Fi, 5G, and satellite network scenarios demonstrate that Flex-QUIC significantly improves throughput and latency reduction compared to the standard QUIC and other enhanced QUIC variants, highlighting its adaptability to diverse and dynamic network conditions. Finally, we further discuss open issues in deploying AI-native transport protocols.

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.

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.

Residual loop-interference (LI) poses a significant challenge for the full-duplex (FD) unmanned aerial vehicle (UAV). To address the issue of residual LI, this paper proposes an amplify-and-forward (AaF) FD-UAV relay system based on a novel orthogonal frequency division multiplexing (OFDM) technique, in which a signal model of infinite impulse response (IIR) is established, instead of the classical finite impulse response (FIR). In the proposed scheme, the residual LI is considered a useful signal and can be combined with the novel OFDM to establish the IIR signal model. Meanwhile, the guard interval (GI) is designed to maintain the circular convolution structure, which differs from the cyclic prefix (CP) applied by the classical OFDM. At the receiver, the IIR signals are influenced only by Gaussian white noise. The proposed FD-UAV relay system can maintain a satisfactory bit error rate (BER) even in the presence of significant residual LI, compared to conventional solutions for suppressing LI on FD-UAV relay. Numerical simulations validate that our proposed scheme offers a fresh solution to the residual LI problem in FD-UAV communication.

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.

Reconfigurable intelligent surfaces (RISs) not only assist communication but also help the localization of user equipment (UE). This study focuses on indoor localization of UE with a single access point (AP) and multiple RISs. First, we propose a two-stage channel estimation scheme where RIS phase shifts are tuned to obtain multiple channel soundings. In the first stage, the newtonized orthogonal matching pursuit algorithm extracts the parameters of multiple paths from the received signals. Then, the LOS path and RIS-reflected paths are identified. In the second stage, the estimated path gains of RIS-reflected paths with different phase shifts are utilized to determine the angle of arrival (AOA) at the RIS by obtaining the angular pseudo spectrum. Consequently, by taking the AP and RISs as reference points, the linear least squares estimator can locate UE with the estimated AOAs. Simulation results show that the proposed algorithm can realize centimeter-level localization accuracy in the discussed scenarios. Moreover, the higher accuracy of pseudo spectrum, a larger number of channel soundings, and a larger number of reference points can realize higher localization accuracy of UE.

With the gradual penetration of the internet of things (IoT) into all areas of life, the scale of IoT devices shows an explosive growth trend. The era of internet of everything is coming, and the important position of IoT security is becoming increasingly prominent. Due to the large number types of IoT devices, there may be different security vulnerabilities, and unknown attack forms and virus samples are appear. In other words, large number of IoT devices, large data volumes, and various attack forms pose a big challenge of malicious traffic identification. To solve these problems, this paper proposes a concept drift detection and adaptation (CDDA) method for IoT security framework. The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection, so as to select the best AI model. The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice, and the effect on the performance of IoT traffic identification is also verified.

Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking (TSN). While time synchronization errors cannot be overlooked, pursuing minimal time errors may incur unnecessary costs. Using complex network theory, this study proposes a hierarchy for TSN and introduces the concept of bounded time error. A coupling model between traffic scheduling and time synchronization is established, deriving functional relationships among end-to-end delay, delay jitter, gate window, and time error. These relationships illustrate that time errors can trigger jumps in delay and delay jitter. To evaluate different time errors impact on traffic scheduling performance, an end-to-end transmission experiment scheme is designed, along with the construction of a TSN test platform implementing two representative cases. Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network. Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches. Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors, up to one gating cycle. End-to-end delay jitter tends to increase with the number of hops. When the ratio of time error bound to window exceeds 10%, the number of schedulable traffic flows decreases rapidly. Case B reveals that when time error is below 1 $\mu$ s, the number of schedulable traffic flows begins to increase significantly, approaching full schedulability at errors below 0.6 $\mu$ s.

With the rapid development of intelligent cyber-physical systems (ICPS), diverse services with varying Quality of Service (QoS) requirements have brought great challenges to traditional network resource allocation. Furthermore, given the open environment and a multitude of devices, enhancing the security of ICPS is an urgent concern. To address these issues, this paper proposes a novel trusted virtual network embedding (T-VNE) approach for ICPS based combining blockchain and edge computing technologies. Additionally, the proposed algorithm leverages a deep reinforcement learning (DRL) model to optimize decision-making processes. It employs the policy-gradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search (BFS) algorithm to determine the optimal embedding paths. Finally, through simulation experiments, the efficacy of the proposed method was validated, demonstrating outstanding performance in terms of security, revenue generation, and virtual network request (VNR) acceptance rate.

Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, cloud, and clean RAN (C-RAN), this article proposes a novel cooperative, intelligent, and service-based RAN (CIS-RAN) architecture. Focusing on cooperation, CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence (AI). To improve both performance and effectiveness across diverse application scenarios, CIS-RAN enhances network cooperation throughout the entire process of acquisition, transmission, and processing, thereby enabling efficient information acquisition, diverse cooperative interactions, and intelligent fusion decision-making. Key technologies are discussed, with network cooperative multiple-input multiple-output (MIMO) examined as a case study, demonstrating superior performance over traditional architectures, as demonstrated by numerical results. Future research directions are outlined, emphasizing the continued exploration and advancement of the CIS-RAN architecture, particularly in enhancing network cooperation.

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.

Practical byzantine fault tolerance (PBFT) can reduce energy consumption and achieve high throughput compared with the traditional PoW algorithm, which is more suitable for a strongly consistent consortium blockchain. However, due to the frequent communication among nodes, PBFT cannot realize scalability in large-scale networks. Existing PBFT-based algorithms still ignore performance and security. Therefore, we propose a secure and efficient practical byzantine fault tolerance based on double layers and multi copies (DM-PBFT). We design a reputation evaluation and node scheduling method for DM-PBFT. And then we propose an adaptive node scheduling strategy based on the derived threshold values after analyzing the system communication complexity and security. Combining the above research, a node dynamic adjustment mechanism is proposed to freeze or adjust the node operation status according to the system environment. Simulation experiments show that the proposed mechanism can improve efficiency and increase the system's throughput.

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%.

Recently, uniform circular array (UCA) based orbital angular momentum (OAM) beam steering schemes have been proposed to overcome the limitations of coaxial transmission. Unlike the traditional multiple-input-multiple-output (MIMO) beam steering, OAM beam steering includes both the OAM generation and the beam steering. Generally, the true time delay (TTD) or the phase shifter (PS) are required for beam steering in the radio domain. Previous studies suggest that TTD is preferred for wideband MIMO beam steering to avoid beam squint caused by PS. However, in this paper, we theoretically prove that to generate the OAM beam ideally, PS should be used, while TTD deteriorates the mode orthogonality, which is influenced by the relative bandwidth. Once the ideal OAM beam is generated, TTD is required to prevent beam squint. Based on this analysis, we propose to use the two-stage phase-shifting (TSPS) architecture for OAM beam steering: PS for OAM generation and TTD for beam steering. Simulation results suggest that compared to the spectrum efficiency (SE) of PS based OAM communication, the SE based on the TTD significantly declines as the relative bandwidth increases. Furthermore, OAM beam steering using the TSPS architecture greatly outperforms systems that adopt a single TTD or PS network.

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.

In GNSS-denied environments, signals of opportunity (SOP) offer an efficient and passive solution for navigation and positioning by utilizing ambient signals. Nevertheless, conventional SOP techniques face significant challenges in real-time processing, especially under sub-Nyquist sampling conditions, due to high data acquisition rates and off-grid errors. To address this, this paper proposes the signal reconstruction and kernel sparse encoding (SRKSE) model, a novel general framework for high-precision parameter estimation. By combining compressed sensing with a deep unfolding network, the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors. Key innovations of SRKSE include dual cross-attention mechanisms for enhanced feature extraction, sinc sparse kernel encoding to minimize quantization errors, and a custom loss function for balanced optimization. With these advancements, SRKSE achieves up to a 650-fold improvement in time of arrival (TOA) estimation accuracy while operating at just 1% of the Nyquist sampling rate. The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency, especially when operating under sub-Nyquist sampling conditions. Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.

Differential pulse-position modulation (DP PM) can achieve a good compromise between power and bandwidth requirements. However, the output sequence has undetectable insertions and deletions. This paper proposes a successive cancellation (SC) decoding scheme based on the weighted levenshtein distance (WLD) of polar codes for correcting insertions/deletions in DPPM systems. In this method, the WLD is used to calculate the transfer probabilities recursively to obtain likelihood ratios, and the low-complexity SC decoding method is built according to the error characteristics to match the DPPM system. Additionally, the proposed SC decoding scheme is extended to list decoding, which can further improve error correction performance. Simulation results show that the proposed scheme can effectively correct insertions/deletions in the DPPM system, which enhances its reliability and performance.

As investigated by 3GPP, support of UPF (user plane function) onboard satellite can reduce the latency of communications via satellite, and then it becomes a key enhancement in 5G network integrating with satellite communication. However, current 5G system cannot support UPF onboard LEO (low earth orbit) satellites, as it would face challenges like UPF mobility handling, synchronization between mobile network and satellite network, and condition of activating local data switching. To solve such challenges, this paper proposes a solution to support UPF onboard LEO satellite, which consists of enhanced network architecture, I-UPF (intermediate UPF) based local data switching scheme and communication latency based data path selection. We subsequently develop analytic models for performance evaluation and conduct simulations using the constellation configuration of iridium II. The simulation results show that the data switching via I-UPF onboard LEO satellite can reduce E2E (end to end) packet delivery latency and E2E packet loss ratio significantly compared with that of routing the data back to 5GC on the ground. The proposed scheme yet has increased signaling cost for handling UPF mobility.els, compared with existing similar companding algorithms.

Low earth orbit (LEO) satellite networks can provide wider service coverage and lower latency than traditional terrestrial networks, which have attracted considerable attention. However, the uneven distribution of human population and data traffic on the ground incurs unbalanced traffic load in LEO satellite networks. To this end, we propose a load-balancing routing algorithm for LEO satellite networks based on ant colony optimization and reinforcement learning. In the ant colony algorithm, we improve the pheromone update rule by introducing load-aware heuristic information, e.g., the current node transmission overhead, delay and load status, and reinforcement learning-based link quality evaluation. It enables the routing algorithm to select the lightly loaded node as the next hop to balance the network load. We simulate and verify the proposed algorithm using the NS2 simulation platform, and the results show that our algorithm improves the data delivery ratio and throughput while ensuring lower latency and transmission overhead.

Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.

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 deployment of the low earth orbit (LEO) satellites provides a large number of signals of opportunity (SOPs), unmanned aerial vehicle (UAV) positioning and navigation via LEO-SOPs have received much attention. Current research is focused on Doppler positioning techniques, which require the collaboration of multiple satellites ($\geq 3$). However, the dynamic changes of LEO satellites weaken the generalization ability of Doppler positioning. In this paper, a direct position determination (DPD) method with uniform circular array (UCA) is proposed for UAV positioning from the perspective of the spatial spectrum estimation of LEO-SOPs. The proposed method employs the orthogonality between the signal and noise subspaces of the covariance matrix of the different received SOPs to establish the cost function for UAV's coordinate. Instead of the multiple dimensional search, a root mean square propagation (RMSProp) gradient optimizer with an adaptive learning rate is developed to find the coordinate of UAV. The effectiveness and robustness of the proposed method are verified using numerical data generated from the systems tool kit (STK).

An orderly competition mechanism is used to change unexpected competition into predictable competition so as to reduce access collision during access process. The scheme is realized by learning, queuing, and accessing. Queuing is the key step to reduce random and realize orderly competition. Related parameters leading to access random including the arrival rate, the delay requirements, the number of devices, and so on, are defined as queue factors in this paper. The queue factors are obtained from the improved double deep Q network (DDQN) algorithm which is proposed here by setting asynchronous weights of two target networks. By learning, the queue factors will guide the devices with diverse delay requirements to queue. Then the queued devices start the access process according to their learning optimal access slot and preamble. Different from traditional competition solutions, markov decision process of the orderly competition mechanism has only two states, which remarkably cuts down the back-off rate and reduces the access delay. The simulation results show that the access success rate of this method can be close to 100% before the system capacity approaches the maximum value.

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.

Mobile communications are reaching out to every aspect of our daily life, necessitating high-efficiency data transmission and support for diverse data types and communication scenarios. Polar codes have emerged as a promising solution due to their outstanding error-correction performance and low complexity. Unequal error protection (UEP) involves non-uniform error safeguarding for distinct data segments, achieving a fine balance between error resilience and resource allocation, which ultimately enhancing system performance and efficiency. In this paper, we propose a novel class of UEP rateless polar codes. The codes are designed based on matrix extension of polar codes, and elegant mapping and duplication operations are designed to achieve UEP property while preserving the overall performance of conventional polar codes. Superior UEP performance is attained without significant modifications to conventional polar codes, making it straightforward for compatibility with existing polar codes. A theoretical analysis is conducted on the block error rate and throughput efficiency performance. To the best of our knowledge, this work provides the first theoretical performance analysis of UEP rateless polar codes. Simulation results show that the proposed codes significantly outperform existing polar coding schemes in both block error rate and throughput efficiency.

For 5G millimeter wave (mm-Wave) user equipments (UEs), all test cases must be evaluated in Over-The-Air (OTA) manner. Test time increases dramatically compared to Sub-6 GHz. Therefore, test time reduction is of great significance for 5G mm-Wave OTA testing. Among all test cases, beam peak search is the most time-consuming, taking up the majority of the overall test time. Therefore, the objective of this work is to determine a suitable beam peak search grid for 5G mm-Wave UEs with satisfactory accuracy and efficiency. Through radiation property investigation of 5G mm-Wave commercial UEs, more reasonable reference array configuration (4$\times$2) and reference deployment scenario (composite beam) are proposed for beam peak search grid analysis. The effect of different grid configurations on beam peak search precision are characterized quantitatively. The determination of associated measurement uncertainty (MU) term along with quantitative analysis approach are proposed based on statistical analysis. Finally, the recommended minimum number of beam peak search grid points is 182 based on the proposed 4$\times$2 array under composite beam scenario. Compared with currently-required 1106 points in 3GPP / CTIA specifications, over 80% reduction can be achieved without increasing the MU limit. The feasibility of the proposed MU analysis as well as the recommended grids is demonstrated through measurements.

Efficient energy utilization in covert communication sustains covertness while assuring communication quality and efficiency. This paper investigates covert communication energy efficiency (EE) in direct uplink satellite-ground communications, focusing on enhancing system EE via optimized transmit beamforming and satellite orbit altitude selection. This paper first establishes an optimization problem to maximize system EE in a direct uplink satellite-ground covert communication scenario. To solve this non-convex optimization problem, it is decomposed into two subproblems and solved using the successive convex approximation (SCA) method. Based on the above methods, this paper proposes an overall iterative optimization algorithm. Simulation results demonstrate that the proposed algorithm surpasses the conventional baseline algorithms in terms of system EE. Furthermore, they elucidate the correlation between the amount of information received by the receiver and the variations in the satellite's orbital altitude.

Synthetic aperture radar (SAR) radio frequency identification (RFID) localization is widely used for automated guided vehicles (AGVs) in the industrial internet of things (IIoT). However, the AGV’s speeds are limited by the phase difference (PD) of two neighboring readers. In this paper, an inertial navigation system (INS) based SAR RFID localization method (ISRL) where AGV moves nonlinearly. To relax the speed limitation, a new phase-unwrapping method based on the similarity of PDs (PU-SPD) is proposed to deal with the PD ambiguity when the AGV speed exceeds 60km/h. In localization, the gauss-newton algorithm (GN) is employed and an initial value estimation scheme based on variable substitution (IVE-VS) is proposed to improve its positioning accuracy and the convergence rate. Thus, ISRL is a combination of IVE-VS and GN. Moreover, the Cramer-Rao lower bound (CRLB) and the speed limitation is derived. Simulation results show that the ISRL can converge after two iterations, and the positioning accuracy can achieve 7.50cm at a phase noise level $\sigma=0.18$, which is 35% better than the Hyperbolic unbiased estimation localization (HyUnb).

In the wake of major natural disasters or human-made disasters, the communication infrastructure within disaster-stricken areas is frequently damaged. Unmanned aerial vehicles (UAVs), thanks to their merits such as rapid deployment and high mobility, are commonly regarded as an ideal option for constructing temporary communication networks. Considering the limited computing capability and battery power of UAVs, this paper proposes a two-layer UAV cooperative computing offloading strategy for emergency disaster relief scenarios. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm integrated with prioritized experience replay (PER) is utilized to jointly optimize the scheduling strategies of UAVs, task offloading ratios, and their mobility, aiming to diminish the energy consumption and delay of the system to the minimum. In order to address the aforementioned non-convex optimization issue, a Markov decision process (MDP) has been established. The results of simulation experiments demonstrate that, compared with the other four baseline algorithms, the algorithm introduced in this paper exhibits better convergence performance, verifying its feasibility and efficacy.

In this paper, we propose a random access scheme termed sign-compute diversity slotted ALO-HA (SCDSA). The SCDSA scheme combines diversity transmission with compute-and-forward. Without considering the capture effect and multiple user detection techniques, our scheme can reach a high throughput of 0.98 without feedback under finite frame size settings, where the upper bound on performance is 1. Moreover, a lower bound on throughput performance is derived, which is tight in some parameter settings and can be used to approximate theoretical performance. Simulation results validate our analysis and confirm the advantages of our proposed scheme.

Advanced persistent threat (APT) can use malware, vulnerabilities, and obfuscation countermeasures to launch cyber attacks against specific targets, spy and steal core information, and penetrate and damage critical infrastructure and target systems. Also, the APT attack has caused a catastrophic impact on global network security. Traditional APT attack detection is achieved by constructing rules or manual reverse analysis using expert experience, with poor intelligence and robustness. However, current research lacks a comprehensive effort to sort out the intelligent methods of APT attack detection. To this end, we summarize and review the research on intelligent detection methods for APT attacks. Firstly, we propose two APT attack intelligent detection frameworks for endpoint samples and malware, and for malware-generated audit logs. Secondly, this paper divides APT attack detection into four critical tasks: malicious attack detection, malicious family detection, malicious behavior identification, and malicious code location. In addition, we further analyze and summarize the strategies and characteristics of existing intelligent methods for each task. Finally, we look forward to the forefront of research and potential directions of APT attack detection, which can promote the development of intelligent defense against APT attacks.

Text semantic extraction has been envisio-ned as a promising solution to improve the data transmission efficiency with the limited radio resources for the autonomous interactions among machines and things in the future sixth-generation (6G) wireless networks. In this paper, we propose a Chinese text semantic extraction model, namely T-Pointer, to improve the quality of semantic extraction by integrating the Transformer with the pointer-generator network. The proposed T-Pointer model consists of a semantic encoder and a semantic decoder. In the encoding stage, we use the multi-head attention mechanism of the Transformer to extract semantic features from the input Chinese text. In the decoding stage, we first use the Transformer to extract multi-level global text features. Then, we introduce the pointer-generator network model to directly copy the keyword information from the source text. The simulation results demonstrate that the T-Pointer model can improve the bilingual evaluation understudy (BLEU) and recall-oriented understudy for gisting evaluation (ROUGE) by 14.69% and 14.87% on average in comparison with the state-of-the-art models, respectively. Also, we implement the T-Pointer model on a semantic communication system based on the universal software radio peripheral (USRP) platform. The result shows that the packet delay of semantic transmission can be reduced by $52.05\%$ on average, compared to traditional information transmission.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for next-generation communication systems. However, this will expand the near-field (NF) range, rendering more users more likely to be located in the NF region. In this paper, we aim to answer two questions: What are the new characteristics of the NF channel? Is it necessary to develop new transciver techniques to maintain system performance within the NF region? To this end, we first review current NF channel models and analyze the differences between the existing 3GPP TR 38.901 channel model and the NF channel model, including the spherical wavefront and spatially non-stationarity. Then, we provide examples on how these differences affect the XL-MIMO system performance in terms of beamforming gain and achievable rate. Simulation results demonstrate that, when using far-field (FF) technique under the NF channel, the maximum normalized beam gain loss is less than 3 dB for most users in the NF region defined by Rayleigh distance. Moreover, the achievable rate loss of beam training is less than 3% compared to that realized by NF technique. Finally, we demonstrate the necessity of employing NF transceiver techniques based on simulation results.