The large-scale development of the low-altitude economy imposes increasingly stringent requirements on the supporting information infrastructure, necessitating the establishment of a low-altitude intelligent network (LAIN) with wide-area communication, high-precision navigation, and efficient supervision capabilities. Benefiting from its broad coverage, high reliability, and large bandwidth, the 5G cellular network serves as a critical foundation for LAIN construction. However, conventional cellular networks are primarily designed for two-dimensional terrestrial scenarios, and thus face significant limitations in coverage and interference resistance within complex three-dimensional low-altitude environments. To address the unique demands of LAIN applications, key challenges must be tackled, including achieving seamless three-dimensional coverage, mitigating interference in multi-dimensional network deployments, and ensuring stringent requirements for service quality and security supervision. This paper proposes an integrated LAIN architecture characterized by the convergence of communication, navigation, sensing, and management, enhanced with artificial intelligence and security mechanisms to improve overall system intelligence and resilience. Furthermore, this paper conducts an in-depth analysis of the critical challenges in LAIN deployment, explores enabling technologies to address these issues, and offers insights into the future development direction of low-altitude intelligent networks.

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.

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.

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.

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.

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).

As the commercialization of the fifth generation communication (5G) is sped up, its system testing scheme is vital for the successful deployment of 5G. Especially, 5G relies on the scale-increased multiple-input-multiple output (MIMO) technique to improve its capacity and coverage. Thus, testing new functions of the 5G MIMO system accurately and efficiently, including beamforming (beam-tracking with movement) and multiple-user (MU) multiplexing, is a challenging task. This paper tries to construct a laboratorial hardware and conduct equipment-controlled field testing. Firstly, the testing scheme is presented, which is composed of the framework, the channel models and the validation methods. Then, the channel model principles are explained in detail due to its direct influence on the testing accuracy. Specifically, we utilize the spatial consistency and the multi-link correlation properties to emulate the high-speed dynamic time-varying (HDT) and the multiple-cell (MC)-MU-MIMO channels. Finally, the above testing scheme is verified in a Shanghai 5G field experiment with the practical commercial equipment and the channel emulator. The results show that the 5G new functions are tested accurately and efficiently by switching the channel emulation configurations.

This paper presents a novel approach to design a compact circular rat-race coupler with an ultra-wide stopband, with the aim to reduce its size while maintaining performance. The design methodology begins with a common miniaturization technique to replace the conventional quarter-wavelength transmission line with an equivalent low-pass filter loaded with parallel coupled line and radial stubs. Since the latter leads to produce higher order harmonics, parasitic open-ended stubs are then properly introduced in the structure not only to overcome the issue but also to produce controllable transmission zeros. A versatile analytical model is also developed taking into account manufacturing restrictions, which makes it possible to extract the physical parameters of the coupler unit-cell for a given desired compactness percentage with respect to the conventional rat-race coupler. A prototype is fabricated and measured to validate the design, demonstrating the predicted behavior fairly achieved by numerical analysis. A significant size reduction of about 86.1% was achieved compared to the conventional design, while effectively suppressing higher order~modes up to 23.4~GHz (including the 13th harmonic based on $|S_{11}|$$>$$-$5~dB and $|S_{21}|$$<$$-$17~dB) with high isolation level ($|S_{41}|$$<$$-$17~dB) between the ports.

In 5G new radio (NR), polar codes are adopted for eMBB downlink control channels where the blind detection is employed in user equipment (UE) to identify the correct downlink control information (DCI). However, different from that in the 4G LTE system, the cyclic redundancy check (CRC) in polar decoding plays both error correction and error detection roles. Consequently, the false alarm rates (FAR) may not meet the system requirements (FAR$<1.52 \times 10^{-5}$). In this paper, to mitigate the FAR in polar code blind detection, we attach a binary classifier after the polar decoder to further remove the false alarm results and meanwhile retain the correct DCI. This classifier works by tracking the squared Euclidean distance ratio (SEDR) between the received signal and hypothesis. We derive an analytical method to fast compute proper classification threshold that is implementation-friendly in practical use. Combining the well-designed classifier, we show that some very short CRC sequences can even be used to meet the FAR requirements. This consequently reduces the CRC overhead and contributes to the system error performance improvements.

This paper considers a multi-antenna access point (AP) transmitting secrecy message to a single-antenna user in the presence of a single-antenna illegal eavesdropper (Eve) and proposes a double active reconfigurable intelligent surfaces (DARISs) assisted physical layer security (PLS) scheme denoted by DARISs-PLS to protect the secrecy message transmission. We formulate a secrecy rate maximization problem for the proposed DARISs-PLS scheme by considering a power budget constraint for the two active reconfigurable intelligent surfaces (ARISs) and AP. To address the formulated optimization problem, we jointly optimize the reflecting coefficients for the two ARISs and the beamforming at the AP in an iterative manner by applying Dinkelbach based alternating optimization (AO) algorithm and a customized iterative algorithm together with the semidefinite relaxation (SDR). Numerical results reveal that the proposed DARISs-PLS scheme outperforms the double passive reconfigurable intelligent surfaces-assisted PLS method (DPRISs-PLS) and single ARIS-assisted PLS method (SARIS-PLS) in terms of the secrecy rate.

The quality of spectrum sensing plays a significant role in determining the outage probability during the data transmission phase in an interweave cognitive radio network. If the secondary user (SU) fails to detect the primary user (PU) activity, it can result in interference that limits the system performance. Additionally, since the wireless medium is broadcast in nature, there is a risk of eavesdroppers intercepting the cognitive users' data. Therefore, it is crucial to consider secrecy in the system analysis. In this paper, we analyze the secrecy outage probability (SOP) at the secondary receiver and derive the secret diversity gain for an interweave cognitive multiple-input multiple-output (MIMO) fading channel in the presence of an eavesdropper. Our study takes into account the effects of the fading channel, the PU interference, and the eavesdropper on both spectrum sensing and data transmission phases. We demonstrate that utilizing all the antennas for sensing eliminates the limiting effects of missed detection probability and PU interference on the secret diversity gain. As a result, the cognitive user can achieve the same level of secret diversity gain as a conventional non-cognitive system (CNCS). Our analytical results are further validated through simulations.

A wideband low-profile aperture-coupled antenna based on a novel dual-mode-composite scheme is presented. The mode-composite scheme where the TM$_{10}$ cavity mode and the TE$_{121}$ dielectric resonator (DR) mode are combined offers an approach to obtain a wide bandwidth accompanied by stable unidirectional radiation and high efficiency. The use of a lengthened coupling aperture that supports the one-wavelength resonance in the band of interest is an effective feed method of simultaneously exciting the two composite modes without compromising the increased complexity of the antenna geometry. An impedance bandwidth of 49\% for $|$S$_{11}|$ of less than -10 dB, a maximum gain of 10.8 dBi, and stable radiation patterns with low cross-polarization are realized experimentally by a fabricated prototype. Considering the simplicity of the geometry, the wide bandwidth that can cover n77, n78, and n79 bands for the fifth generation (5G) mobile communications and the satisfying radiation performance, the proposed antenna would be a promising candidate for advanced wireless applications.

Compared to high-resolution digital-to-analog converters (DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output (MU-MIMO) system. However, the feasible domain of a 1-bit transmitting signal is non-continuous, and thus it is more challenging to exploit multi-user interference (MUI) by precoding. In this paper, to improve symbol decision accuracy, we investigate MUI exploitation 1-bit precoding methods for massive MU-MIMO systems under QAM modulations. Because MUIs may be constructive or destructive, we define a modified mean square error (MSE) metric for QAM constellations to jointly evaluate the effect of both MUIs and noise. Then, we model the 1-bit precoding optimization problems to minimize the sum modified MSE or the maximum modified MSE, where both the transmitting vector and receiving processing factor are optimization variables. Based on whether the receiving processing factor remains constant during the whole transmission block, two scenarios are taken into consideration. Referring to existing interference exploitation 1-bit precoding methods, we design efficient algorithms to solve the two modified MSE based problems. Compared to existing 1-bit precoding methods, our proposed methods provide better bit error rate performance, especially in more practical scenario II (constant receiving processing factor in one block).

Research on wide area ad hoc networks is of great significance due to its application prospect in long-range networks such as aeronautical and maritime networks, etc. The design of MAC protocols is one of the most important parts impacting the whole network performance. In this paper, we propose a distributed TDMA-based MAC protocol called Dynamic Self Organizing TDMA (DSO-TDMA) for wide area ad hoc networks. DSO-TDMA includes three main features: (1) In a distributed way, nodes in the network select transmitting slots according to the congestion situation of the local air interface. (2) In a self-organization way, nodes dynamically adjust the resource occupancy ratio according to the queue length of neighbouring nodes within two-hop range. (3) In a piggyback way, the control information is transmitted together with the payload to reduce the overhead. We design the whole mechanisms, implement them in NS-3 and evaluate the performance of DSO-TDMA compared with another dynamic TDMA MAC protocol, EHR-TDMA. Results show that the end-to-end throughput of DSO-TDMA is at most 51.4% higher than that of EHR-TDMA, and the average access delay of DSO-TDMA is at most 66.05% lower than that of EHR-TDMA.

In response to the current gaps in effective proactive defense methods within application security and the limited integration of security components with applications, this paper proposes a biomimetic security model, called NeuroShield, specifically designed for web applications. Inspired by the "perception-strategy-effect-feedback" mechanism of the human nervous control system, the model integrates biomimetic elements akin of neural receptors and effectors into applications. This integration facilitates a multifaceted approach to security: enabling data introspection for detailed perception and regulation of application behavior, providing proactive defense capabilities to detect and block security risks in real-time, and incorporating feedback optimization to continuously adjust and enhance security strategies based on prevailing conditions. Experimental results affirm the efficacy of this neural control mechanism-based biomimetic security model, demonstrating a proactive defense success rate exceeding 95%, thereby offering a theoretical and structural foundation for biomimetic immunity in web applications.

In recent years, universal adversarial perturbation (UAP) has attracted the attention of many researchers due to its good generalization. However, in order to generate an appropriate UAP, current methods usually require either accessing the original dataset or meticulously constructing optimization functions and proxy datasets. In this paper, we aim to eliminate any dependency on proxy datasets and explore a method for generating Universal Adversarial Perturbations (UAP) on a single image. After revisiting research on UAP, we discovered that the key to generating UAP lies in the accumulation of Individual Adversarial Perturbation (IAP) gradient, which prompted us to study the method of accumulating gradients from an IAP. We designed a simple and effective process to generate UAP, which only includes three steps: precessing, generating an IAP and scaling the perturbations. Through our proposed process, any IAP generated on an image can be constructed into a UAP with comparable performance, indicating that UAP can be generated free of data. Extensive experiments on various classifiers and attack approaches demonstrate the superiority of our method on efficiency and aggressiveness.

Unmanned aerial vehicles (UAVs) are widely used in commercial activities due to their low cost and high efficiency. However, many unscrupulous activities have taken advantage of the open nature of civil navigation messages to implement Global Navigation Satellite System (GNSS) spoofing attacks on UAVs, resulting in major security risks for UAVs. In order to cope with spoofing attacks, a navigation message authentication (NMA) scheme has been proposed to protect navigation messages. However, the mainstream NMA schemes have defects of a single authentication process and large time overhead. Furthermore, these schemes depend on digital certificates to update the key. In the event that the UAV is disconnected from the certificate authority, it will be unable to update the key in an appropriate manner, which will consequently affect subsequent authentication.To solve the above problems, this paper proposes a hybrid authentication scheme based on BeiDou-III navigation system (BDS-III), which combines Navigation Message Authentication (NMA) and timed efficient stream loss-tolerant authentication (TESLA) to achieve a triple authentication of signatures, TESLA keys, and message authentication code (MAC). Then, the SM2 signature algorithm is modified to reduce the authentication time overhead. Finally, the stability of the key update process is enhanced by storing the public key in the navigation message and broadcasting it by satellite. Experimental verification shows that the proposed scheme can effectively resist replay attacks and generative spoofing attacks, while guaranteeing the security. The authentication error rate of the proposed scheme can meet the demand of less than $10^{-3}$ when it is lower than the average carrier-to-noise ratio of 7dBHz. The time between authentications (TBA) is 15.11 seconds. The unpredictable symbol rate (USR) is 16.8% which is a better performance compared with same kind of scheme.

The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking (TSN) from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments. When multiple TSN networks interconnect over non-TSN networks, all devices in the network need to be synchronized by sharing a uniform time reference. However, most non-TSN networks are best-effort. Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization. These factors can degrade synchronization performance. Therefore, cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks. This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN (SD-TSN) paradigm. It utilizes a combined control plane constructed by a coordinate controller and a domain controller for centralized control and management of cross-domain time synchronization. The general operation flow of the cross-domain time synchronization process is designed. The mechanism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method. A TSN cross-domain prototype testbed is constructed for verification. Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and stability.

Web data extraction has become a key technology for extracting valuable data from websites. At present, most extraction methods based on rule learning, visual pattern or tree matching have limited performance on complex web pages. Through analyzing various statistical characteristics of HTML elements in web documents, this paper proposes, based on statistical features, an unsupervised web data extraction method——traversing the HTML DOM parse tree at first, calculating and generating the statistical matrix of the elements, and then locating data records by clustering method and heuristic rules that reveal inherent links between the visual characteristics of the data recording areas and the statistical characteristics of the HTML nodes——which is both suitable for data records extraction of single-page and multi-pages, and it has strong generality and needs no training. The experiments show that the accuracy and efficiency of this method are equally better than the current data extraction method.

In this paper, we investigate the problem of fast spectrum sharing in vehicle-to-everything communication. In order to improve the spectrum efficiency of the whole system, the spectrum of vehicle-to-infrastructure links is reused by vehicle-to-vehicle links. To this end, we model it as a problem of deep reinforcement learning and tackle it with proximal policy optimization. A considerable number of interactions are often required for training an agent with good performance, so simulation-based training is commonly used in communication networks. Nevertheless, severe performance degradation may occur when the agent is directly deployed in the real world, even though it can perform well on the simulator, due to the reality gap between the simulation and the real environments. To address this issue, we make preliminary efforts by proposing an algorithm based on meta reinforcement learning. This algorithm enables the agent to rapidly adapt to a new task with the knowledge extracted from similar tasks, leading to fewer interactions and less training time. Numerical results show that our method achieves near-optimal performance and exhibits rapid convergence.

As an essential element of intelligent transport systems, Internet of vehicles (IoV) has brought an immersive user experience recently. Meanwhile, the emergence of mobile edge computing (MEC) has enhanced the computational capability of the vehicle which reduces task processing latency and power consumption effectively and meets the quality of service requirements of vehicle users. However, there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost. Unmanned aerial vehicles (UAVs) equipped with MEC servers have become a promising approach for providing communication and computing services to mobile vehicles. Hence, in this article, an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented. Through joint consideration of computational offloading decisions and computational resource allocation, the optimization problem of our proposed architecture is presented to reduce system energy consumption and delay. For purpose of tackling this issue, the original non-convex issue is converted into a convex issue and the alternating direction method of multipliers-based distributed optimal scheme is developed. The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes, and the convergence of the proposed scheme is also significant.

With miscellaneous applications generated in vehicular networks, the computing performance cannot be satisfied owing to vehicles' limited processing capabilities. Besides, the low-frequency (LF) band cannot further improve network performance due to its limited spectrum resources. High-frequency (HF) band has plentiful spectrum resources which is adopted as one of the operating bands in 5G. To achieve low latency and sustainable development, a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side, or at macro-cell base station or at road side unit through LF or HF band to achieve stable and high-speed task offloading. Moreover, a utility function including latency and energy consumption is minimized by optimizing computing and spectrum resources, transmission power and task scheduling. Owing to its non-convexity, an iterative optimization algorithm is proposed to solve it. Numerical results evaluate the performance and superiority of the scheme, proving that it can achieve efficient edge computing in vehicular networks.

Federated learning combined with edge computing has greatly facilitated transportation in real-time applications such as intelligent traffic systems. However, synchronous federated learning is inefficient in terms of time and convergence speed, making it unsuitable for high real-time requirements. To address these issues, this paper proposes an Adaptive Waiting time Asynchronous Federated Learning (AWTAFL) based on Dueling Double Deep Q-Network (D3QN). The server dynamically adjusts the waiting time using the D3QN algorithm based on the current task progress and energy consumption, aiming to accelerate convergence and save energy. Additionally, this paper presents a new federated learning global aggregation scheme, where the central server performs weighted aggregation based on the freshness and contribution of client parameters. Experimental simulations demonstrate that the proposed algorithm significantly reduces the convergence time while ensuring model quality and effectively reducing energy consumption in asynchronous federated learning. Furthermore, the improved global aggregation update method enhances training stability and reduces oscillations in the global model convergence.