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.

The integrated optical true time delay phased array antenna system has the advantages of high bandwidth, small size, low loss and strong anti-interference capability, etc. The high integration of the optically controlled phased array antenna system is a necessary trend for the future development of the phased array, and it is also a major focus and difficulty in the current research of integrated microwave photonics. This paper firstly introduces the basic principle and development history of optical true time delay phased array antenna system based on microwave photonics, and briefly introduces the main implementation methods and integration platform of optical true time delay. Then, the application and development prospect of optical true time delay technology in beam control of phased array antenna system are mainly presented. Finally, according to the current research progress, the possible research directions of integrated optically controlled phased array antenna systems in the future are proposed.

(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.

Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology, including communication. In this review, we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization, signal processing, and machine learning for communication. First, the basic principle of quantum computing, quantum physics systems, and quantum algorithms are analyzed. Then, based on the classification of quantum algorithms, several important basic quantum algorithms, quantum optimization algorithms, and quantum machine learning algorithms are discussed in detail. Finally, the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed, which provides a reference to address computational bottlenecks in communication networks.

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.

Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multi-channel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM (Bi-LSTM), named TSB. Specifically, we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture. The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer. The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data. We have conducted extensive experiments on a dataset generated by a real simulation platform. The results show that the proposed algorithm performs better than the baselines.

In this paper, we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface (ARIS)-assisted multi-antenna jamming (MAJ) scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission. In order to strike a balance between the jamming performance and the energy consumption, we consider a so-called jamming energy efficiency (JEE) which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption. We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer's beamforming vector and ARIS's reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user's detection threshold. To address the non-convex optimization problem, we propose the Dinkelbach-based alternating optimization (AO) algorithm by applying the semidefinite relaxation (SDR) algorithm with Gaussian randomization method. Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface (PRIS)-assisted multi-antenna jamming (PRIS-MAJ) scheme and the conventional multi-antenna jamming scheme without RIS (NRIS-MAJ) in terms of the JEE.

Memory-unsafe programming languages, such as C/C++, are often used to develop system programs, rendering the programs susceptible to a variety of memory corruption attacks. Among these threats, just-in-time return-oriented programming (JIT-ROP) stands out as an advanced method for conducting code-reuse attacks, effectively circumventing code randomization safeguards. JIT-ROP leverages memory disclosure vulnerabilities to obtain reusable code fragments dynamically and assemble malicious payloads dynamically. In response to JIT-ROP attacks, several re-randomization implementations have been developed to prevent the use of disclosed code. However, existing re-randomization methods require recurrent re-randomization during program runtime according to fixed time windows or specific events such as system calls, incurring significant runtime overhead.
In this paper, we present the design and implementation of \mytool, an efficient re-randomization approach on the AArch64 platform. Unlike previous methods that necessitate frequent runtime re-randomization or reply on unreliable triggering conditions, this approach triggers the re-randomization process by detecting the code page harvest operation, which is a fundamental operation of the JIT-ROP attacks, making our method more efficient and reliable than previous approaches. We evaluate \mytool\ on benchmarks and real-world applications. The evaluation results show that our approach can effectively protect programs from JIT-ROP attacks while introducing marginal runtime overhead.

The Internet of Unmanned Aerial Vehicles (I-UAVs) is expected to execute latency-sensitive tasks, but limited by co-channel interference and malicious jamming. In the face of unknown prior environmental knowledge, defending against jamming and interference through spectrum allocation becomes challenging, especially when each UAV pair makes decisions independently. In this paper, we propose a cooperative multi-agent reinforcement learning (MARL)-based anti-jamming framework for I-UAVs, enabling UAV pairs to learn their own policies cooperatively. Specifically, we first model the problem as a model-free multi-agent Markov decision process (MAMDP) to maximize the long-term expected system throughput. Then, for improving the exploration of the optimal policy, we resort to optimizing a MARL objective function with a mutual-information (MI) regularizer between states and actions, which can dynamically assign the probability for actions frequently used by the optimal policy. Next, through sharing their current channel selections and local learning experience (their soft Q-values), the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others' actions. Our simulation results show that for both sweep jamming and Markov jamming patterns, the proposed scheme outperforms the benchmarkers in terms of throughput, convergence and stability for different numbers of jammers, channels and UAV pairs.

This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks (WCNs). We propose a novel jamming channel allocation and power decision-making (JCAPD) approach based on multi-agent deep reinforcement learning (MADRL). In high-dynamic and multi-target aviation communication environments, the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information. This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning (DRL) approaches. In response, we design a distributed multi-agent decision architecture (DMADA). We formulate multi-jammer resource allocation as a multi-agent Markov decision process (MDP) and propose a fingerprint-based double deep Q-Network (FBDDQN) algorithm for solving it. Each jammer functions as an agent that interacts with the environment in this framework. Through the design of a reasonable reward and training mechanism, our approach enables jammers to achieve distributed cooperation, significantly improving the jamming success rate while considering jamming power cost, and reducing the transmission rate of links. Our experimental results show the FBDDQN algorithm is superior to the baseline methods.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is regarded as a promising paradigm for enhancing the connectivity and reliability of non-orthogonal multiple access (NOMA) networks. However, the transmission of STAR-RIS enhanced NOMA networks performance is severely limited due to the inter-user interference (IUI) on multi-user detections. To mitigate this drawback, we propose a generalized quadrature spatial modulation (GQSM) aided STAR-RIS in conjunction with the NOMA scheme, termed STAR-RIS-NOMA-GQSM, to improve the performance of the corresponding NGMA network. By STAR-RIS-NOMA-GQSM, the information bits for all users in transmission and reflection zones are transmitted via orthogonal signal domains to eliminate the IUI so as to greatly improve the system performance. The low-complexity detection and upper-bounded bit error rate (BER) of STAR-RIS-NOMA-GQSM are both studied to evaluate its feasibility and performance. Moreover, by further utilizing index modulation (IM), we propose an enhanced STAR-RIS-NOMA-GQSM scheme, termed E-STAR-RIS-NOMA-GQSM, to enhance the transmission rate by dynamically adjusting reflection patterns in both transmission and reflection zones. Simulation results show that the proposed original and enhanced scheme significantly outperform the conventional STAR-RIS-NOMA and also confirm the precision of the theoretical analysis of the upper-bounded BER.

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.

Recently, a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit (LEO) satellite constellation (SatCon) networks with the vision of providing everywhere Internet coverage from space. Several players have started the deployment phase with different scales. However, the implementation is in its infancy, and many investigations are needed. This work provides an overview of the state-of-the-art architectures, orbital patterns, top players, and potential applications of SatCon networks. Moreover, we discuss new open research directions and challenges for improving network performance. Finally, a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access (NOMA) technologies for improving the achievable capacity of satellite end-users.

New communication systems require high spectral and energy efficiencies to meet the growing demand for services in future networks. In this paper, an efficient multiple parallel reconfigurable intelligent surfaces (RIS)-assisted multiuser (MU) multiple input-multiple output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system is presented. In the transmitter, the proposed N-RIS-MU-MIMO-DQSM system uses a modified block diagonalization technique and a genetic algorithm (GA) to jointly design the precoding signals required at the base station (BS) and the optimal phase changes required at multiple RISs. A reduced detection complexity and improved bit error rate (BER) performance are achieved by incorporating spatial modulation. The proposed system is compared under the same conditions and parameters with two reference systems, considering blind and optimized RISs approaches over correlated Rayleigh fading channels. Results show that compared with a similar system that does not use RISs, the proposed system has up to 30 dB gain in BER performance. Compared with a similar system based on conventional quadrature amplitude modulation (QAM), the proposed system has gains of up to 2-3 dB in BER performance and up to 55.8% lower detection complexity for the analyzed cases.

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.

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.

In this paper, an index modulation (IM) aided uplink orthogonal time frequency space modulation (OTFS) structure for sparse code multiple access (SCMA) is proposed. To be more specific, the information bits are firstly partitioned for transmit antenna (TA) selection and sparse codeword mapping, respectively. Subsequently, the codewords deployed on the 2-dimensional (2D) delay-Doppler (DD) plane are transmitted by the selected TA, and the superimposed signals are jointly detected at the receiver. Furthermore, a low-complexity zero-embedded expectation propagation (ZE-EP) detector is conceived, where the codebooks are extended with zero vectors to reflect the silent indices. The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.

Reconfigurable intelligent surface (RIS) has proven to be promising for future wireless communication. Due to its ability to manipulate electromagnetic (EM) waves, RIS provides a flexible and programmable way to implement intelligent wireless environments. While path loss modeling has been conducted in some prior research, an issue remaining unknown is the characteristics of multi-beam path loss for RIS. In this paper, we model, simulate and measure the multi-beam path loss in RIS-assisted broadcast communication scenarios. We propose two specific configurations of RIS and derive the path loss models, which reveal that the incident beam can be equally divided into multiple beams without power loss through rational design of the phase coding. The proposed path loss model is validated through simulation subsequently. To further verify our conclusions, we build a millimeter wave (mmWave) measurement system with a 35 GHz fabricated RIS. The measurement result corresponds well with the simulation, which shows a difference of about 3 dB in the received signal power of quad-beam compared with dual-beam, as well as dual-beam compared with single-beam, except for the impact of radiation patterns of the antennas and RIS elements.

This paper investigates the uplink spectral efficiency of distributed cell-free (CF) massive multiple-input multiple-output (mMIMO) networks with correlated Rayleigh fading channels based on three different channel estimation schemes. Specifically, each access point (AP) first uses embedded pilots to estimate the channels of all users based on minimum mean-squared error (MMSE) estimation. Given the high computational cost of MMSE estimation, the low-complexity element-wise MMSE (EW-MMSE) channel estimator and the least-squares (LS) channel estimator without prior statistical information are also analyzed. To reduce non-coherent and coherent interference during uplink payload data transmission, simple centralized decoding (SCD) and large-scale fading decoding (LSFD) are examined. Then, the closed-form expressions for uplink spectral efficiency (SE) using MMSE, EW-MMSE, and LS estimators are developed for maximum ratio (MR) combining under LSFD, where each AP may have any number of antennas. The sum SE maximization problem with uplink power control is formulated. Since the maximization problem is non-convex and challenging, a block coordinate descent approach based on the weighted MMSE method is used to get the optimal local solution. Numerical studies demonstrate that LSFD and efficient uplink power control can considerably increase SE in distributed CF mMIMO networks.

In this paper, a novel wideband 8-element multiple-input and multiple-output (MIMO) antenna based on Booker’s relation is proposed for the fifth generation (5G) handset applications. The 8 antenna elements are arranged symmetrically along the two longer vertical side-edge frames of the handset. Each antenna element is composed of a monopole and a slot radiation structure, in which wideband characteristic covering 3140-5620MHz can be obtained. Note that the L-shaped monopole and the slot can be deemed as complementary counterparts approximatively. Furthermore, the \textit{Z}-parameter of the proposed wideband antenna element is equivalent to the shunt impedance of monopole as well as slot radiator. Based on Booker’s relation, the wideband input impedance characteristic is therein achieved compared with conventional wideband technique such as multi-resonance. Four L-shaped stubs as well as two slots etched on the ground plane are utilized to achieve acceptable isolation performance better than 13 dB, with total efficiency higher than 60\% and envelope correlation coefficients (ECCs) lower than 0.1. The proposed antenna scheme can be a good candidate for 5G handset applications with the advantages of wideband, simple structure, high efficiency, and acceptable isolation performance. Also, the scheme might be a rewarding attempt to promote the Booker’s relation in the application of 5G terminal MIMO antenna designs.

In recent years, deep learning-based semantic communications have shown great potential to enhance the performance of communication systems. This has led to the belief that semantic communications represent a breakthrough beyond the Shannon paradigm and will play an essential role in future communications. To narrow the gap between current research and future vision, after an overview of semantic communications, this article presents and discusses ten fundamental and critical challenges in today's semantic communication field. These challenges are divided into theory foundation, system design, and practical implementation. Challenges related to the theory foundation including semantic capacity, entropy, and rate-distortion are discussed first. Then, the system design challenges encompassing architecture, knowledge base, joint semantic-channel coding, tailored transmission scheme, and impairment are posed. The last two challenges associated with the practical implementation lie in cross-layer optimization for networks and standardization. For each challenge, efforts to date and thoughtful insights are provided.

The progress of modern industry has given rise to great requirements for network transmission latency and reliability in domains such as smart grid and intelligent driving. To address these challenges, the concept of Time-sensitive networking (TSN) is proposed by IEEE 802.1TSN working group. In order to achieve low latency, Cyclic queuing and forwarding (CQF) mechanism is introduced to schedule Time-triggered (TT) flows. In this paper, we construct a TSN model based on CQF and formulate the flow scheduling problem as an optimization problem aimed at maximizing the success rate of flow scheduling. The problem is tackled by a novel algorithm that makes full use of the characteristics and the relationship between the flows. Firstly, by K-means algorithm, the flows are initially partitioned into subsets based on their correlations. Subsequently, the flows within each subset are sorted by a new special criteria extracted from multiple features of flow. Finally, a flow offset selecting method based on load balance is used for resource mapping, so as to complete the process of flow scheduling. Experimental results demonstrate that the proposed algorithm exhibits significant advantages in terms of scheduling success rate and time efficiency.

The sixth-generation (6G) networks will consist of multiple bands such as low-frequency, mid-frequency, millimeter wave, terahertz and other bands to meet various business requirements and networking scenarios. The dynamic complementarity of multiple bands are crucial for enhancing the spectrum efficiency, reducing network energy consumption, and ensuring a consistent user experience. This paper investigates the present researches and challenges associated with deployment of multi-band integrated networks in existing infrastructures. Then, an evolutionary path for integrated networking is proposed with the consideration of maturity of emerging technologies and practical network deployment. The proposed design principles for 6G multi-band integrated networking aim to achieve on-demand networking objectives, while the architecture supports full spectrum access and collaboration between high and low frequencies. In addition, the potential key air interface technologies and intelligent technologies for integrated networking are comprehensively discussed. It will be a crucial basis for the subsequent standards promotion of 6G multi-band integrated networking technology.

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.

Multimedia semantic communication has been receiving increasing attention due to its significant enhancement of communication efficiency. Semantic coding, which is oriented towards extracting and encoding the key semantics of video for transmission, is a key aspect in the framework of multimedia semantic communication. In this paper, we propose a facial video semantic coding method with low bitrate based on the temporal continuity of video semantics. At the sender's end, we selectively transmit facial keypoints and deformation information, allocating distinct bitrates to different keypoints across frames. Compressive techniques involving sampling and quantization are employed to reduce the bitrate while retaining facial key semantic information. At the receiver's end, a GAN-based generative network is utilized for reconstruction, effectively mitigating block artifacts and buffering problems present in traditional codec algorithms under low bitrates. The performance of the proposed approach is validated on multiple datasets, such as VoxCeleb and TalkingHead-1kH, employing metrics such as LPIPS, DISTS, and AKD for assessment. Experimental results demonstrate significant advantages over traditional codec methods, achieving up to approximately 10-fold bitrate reduction in prolonged, stable head pose scenarios across diverse conversational video settings.

Abstract: Sparse code multiple access (SCMA) is a non-orthogonal multiple access (NOMA) scheme based on joint modulation and spread spectrum coding. It is ideal for future communication networks with a massive number of nodes due to its ability to handle user overload. Introducing SCMA into visible light communication (VLC) systems can improve the data transmission capability of the system. However, designing a suitable codebook becomes a challenging problem when addressing the demands of massive connectivity scenarios. Therefore, this paper proposes a low-complexity design method for high-overload codebooks based on the minimum bit error rate (BER) criterion. Firstly, this paper constructs a new codebook with parameters based on the symmetric mother codebook structure by allocating the codeword power so that the power of each user codebook is unbalanced; then, the BER performance in the visible light communication system is optimized to obtain specific parameters; finally, the successive interference cancellation (SIC) detection algorithm is used at the receiver side. Simulation results show that the method proposed in this paper can converge quickly by utilizing a relatively small number of detection iterations. This can simultaneously reduce the complexity of design and detection, outperforming existing design methods for massive SCMA codebooks.% so as to reduce the out-of-band (OOB) radiation as much as possible. Parameters of the proposed scheme are solved under joint con-straints of constant power and unity cumulative distribution. A new receiving method is also proposed to improve the bit error rate (BER) performance of OFDM systems. Simulation results indicate the proposed scheme can achieve better OOB radiation and BER performance at same PAPR levels, compared with existing similar companding algorithms.

In this paper, we present a novel particle filter (PF)-based direct position tracking method utilizing multiple distributed observation stations. Traditional passive tracking methods are anchored on repetitive position estimation, where the set of consecutive estimates provides the tracking trajectory, such as Two-step and direct position determination methods. However, duplicate estimates can be computationally expensive. In addition, these techniques suffer from data association problems. The PF algorithm is a tracking method that avoids these drawbacks, but the conventional PF algorithm is unable to construct a likelihood function from the received signals of multiple observatories to determine the weights of particles. Therefore, we developed an improved PF algorithm with the likelihood function modified by the projection approximation subspace tracking with deflation (PASTd) algorithm. The proposed algorithm uses the projection subspace and spectral function to replace the likelihood function of PF. Then, the weights of particles are calculated jointly by multiple likelihood functions. Finally, the tracking problem of multiple targets is solved by multiple sets of particles. Simulations demonstrate the effectiveness of the proposed method in terms of computational complexity and tracking accuracy.

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.

Deep learning-based Joint Source-Channel Coding (JSCC) is a crucial component in semantic communication, and recent research has made significant progress in adapting to different channels. In this paper, we propose a multi-stage progressive technique called Deep learning based Progressive Joint Source-Channel Coding (DP-JSCC). This approach partitions the source into multiple stages and transmits the signals continuously. The receiver gradually enhances the quality of image reconstruction by progressively receiving the signals, offering greater flexibility compared to existing dynamic rate transmission methods. The model adopts a lightweight architectural design, where we introduce an efficient module called the Inverted Shuffle Attention Bottleneck (ISAB) and incorporate self-attention mechanisms in the encoding and decoding process to capture signal correlations and establish long-range dependencies. Additionally, we introduce the Progressive Focus Weight Allocation (PFWA) method to improve the image reconstruction capability in progressive transmission tasks. These design enhance the expressive capacity of the model. Simulation results demonstrate that DP-JSCC can flexibly adjust the transmission rate according to requirements without the need for retraining or deployment, enabling continuous optimization of signals at different rates. Furthermore, compared to state-of-the-art JSCC methods, DP-JSCC exhibits advantages in terms of computational complexity, parameter count, and reconstruction performance.

As emerging services continue to be explored, indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues, leading to difficulties in achieving flexible coverage. In this paper, we introduce transmissive reconfigurable intelligent surfaces (RISs) as intelligent passive auxiliary devices into indoor scenes, replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs. Specifically, we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multi-user downlink communication systems. This involves considering more realistic indoor congestion modeling and near-field propagation characteristics. The goal of our optimization is to minimize the total transmit power at the access point (AP) for different user service requirements, including quality-of-service (QoS) and wireless power transfer (WPT). Due to the non-convex nature of the optimization problem, adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming. Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios. Furthermore, the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.

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.

Network architectures assisted by Generative Artificial Intelligence (GAI) are envisioned as foundational elements of sixth-generation (6G) communication system. To deliver ubiquitous intelligent services and meet diverse service requirements, 6G network architecture should offer personalized services to various mobile devices. Federated learning (FL) with personalized local training, as a privacy-preserving machine learning (ML) approach, can be applied to address these challenges. In this paper, we propose a meta-learning-based personalized FL (PFL) method that improves both communication and computation efficiency by utilizing over-the-air computations. Its "pretraining-and-fine-tuning" principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy. Experiment results demonstrate the outperformance and efficacy of the proposed algorithm, and notably indicate enhanced communication efficiency without compromising accuracy.

With the development of wireless communication, the fifth generation mobile communication technology (5G) has emerged as a hot topic in high-speed railway communication system and has moved towards industrial application. Investigating the radio propagation characteristics in 5G high-speed train (HST) scenarios is essential for enhancing wireless coverage and overall system performance. We propose a novel 5G passive sounding scheme to extract channel impulse responses (CIRs) using channel state information reference signals (CSI-RS) from the target 5G base station (BS). Detailed procedures for time-frequency synchronization, CSI-RS detection and extraction are presented through simulations. Through the laboratory work involving absolute power calibration, phase coherence calibration and power delay profile (PDP) validation, we validate the accuracy and performance of the developed platform. Furthermore, a measurement campaign was conducted in HST scenarios encompassing both residential and undeveloped areas. The path loss (PL) model and the channel characteristics including stationarity interval (SI), multi-path components (MPCs), shadow fading (SF), Rician K-factor, root mean square (RMS) delay spread and received correlation coefficients are analyzed and fitted. The estimated channel characteristics and the statistical model presented in this paper will contribute to the research on HST radio propagation and the development of 5G railway communication systems.

Symmetric encryption algorithms learned by the previous proposed end-to-end adversarial network encryption communication systems are deterministic. With the same key and same plaintext, the deterministic algorithm will lead to the same ciphertext. This means that the key in the deterministic encryption algorithm can only be used once, thus the encryption is not practical. To solve this problem, a non-deterministic symmetric encryption end-to-end communication system based on generative adversarial networks is proposed. We design a nonce-based adversarial neural network model, where a "nonce" standing for "number used only once" is passed to communication participants, and does not need to be secret. Moreover, we optimize the network structure through adding Batch Normalization (BN) to the CNNs (Convolutional Neural Networks), selecting the appropriate activation functions, and setting appropriate CNNs parameters. Results of experiments and analysis show that our system can achieve non-deterministic symmetric encryption, where Alice encrypting the same plaintext with the key twice will generate different ciphertexts, and Bob can decrypt all these different ciphertexts of the same plaintext to the correct plaintext. And our proposed system has fast convergence and the correct rate of decryption when the plaintext length is 256 or even longer.

Physical layer security methods based on joint relay and jammer selection (JRJS) have been widely investigated in the study of secure wireless communications. Different from current works on JRJS schemes, which assumed that the global channel state information (CSI) of the eavesdroppers (Eves) was known beforehand, then the optimal relaying and jamming relays were determined. More importantly, the time complexity of selecting optimal jamming relay is $O(N^2)$, where $N$ is the maximum number of relays/Eves. In this paper, for the scenario where the source wants to exchange the message with the destination, via relaying scheme due to longer communication distance and limited transmission power, in the presence of multiple Eves, with the assumption of Eves' perfect CSI and average CSI, we propose two kinds of JRJS methods. In particular, the time complexity of finding the optimal jammer can be reduced to $O(N)$. Furthermore, we present a novel JRJS scheme for no CSI of Eves by minimizing the difference between expected signal and interfering signal at the destination. Finally, simulations show that the designed methods are more effective than JRJS and other existing strategies in terms of security performance.

Named data networking (NDNs) is an idealized deployment of information-centric networking (ICN) that has attracted attention from scientists and scholars worldwide. A distributed in-network caching scheme can efficiently realize load balancing. However, such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity, thus reducing the caching efficiency of NDN routers. To mitigate these caching problems and improve the NDN caching efficiency, in this paper, a hierarchical-based sequential caching (HSC) scheme is proposed. In this scheme, the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels. The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data, improve the data caching efficiency of named data networks, shorten the response time, and reduce cache redundancy. Simulation results show that this scheme can effectively improve the cache hit rate (CHR) and reduce the average request delay (ARD) and average route hop (ARH).

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.

With the continuous advancement of communication and unmanned aerial vehicle (UAV) technologies, the collaborative operations of diverse platforms, including UAVs and ground vehicles, have been significantly promoted. However, battlefield uncertainties, such as equipment failures and enemy attacks, can impact these collaborative operations' stability and communication efficiency. To this end, we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network deployment. It then incorporates the concept of virtual guiding force, enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information, thereby improving network destruction resistance. Simulation results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance.

With the rapid development of blockchain technology, the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard, also known as the state secret algorithm GuoMi algorithm. The original Hyperledger Fabric only supports internationally common encryption algorithms, so it is particularly necessary to enhance support for the national encryption standard. Traditional identity authentication, access control, and security audit technologies have single-point failures, and data can be easily tampered with, leading to trust issues. To address these problems, this paper proposes an optimized and application research plan for Hyperledger Fabric. We study the optimization model of cryptographic components in Hyperledger Fabric, and based on Fabric's pluggable mechanism, we enhance the Fabric architecture with the national encryption standard. In addition, we research key technologies involved in the secure application protocol based on the blockchain. We propose a blockchain-based identity authentication protocol, detailing the design of an identity authentication scheme based on blockchain certificates and Fabric CA, and use a dual-signature method to further improve its security and reliability. Then, we propose a flexible, dynamically configurable real-time access control and security audit mechanism based on blockchain, further enhancing the security of the system.

Federated learning (FL) is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties. When combined with Fog Computing, FL offers enhanced capabilities for machine learning applications in the Internet of Things (IoT). However, implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy, preventing collusion attacks, and ensuring robust data aggregation. To address these challenges, we propose an Efficient Privacy-preserving and Robust Federated Learning (EPRFL) scheme for fog computing scenarios. Specifically, we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm, which is not only resistant to model inference and collusion attacks, but also robust to fog node dropping. Then, we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead. To minimize training delays, we develop a dynamic task scheduling strategy based on comprehensive score. Theoretical analysis demonstrates that EPRFL offers robust security and low latency. Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving, model performance, and resource efficiency.