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

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

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

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

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

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

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

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

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

A covert communication approach in time-frequency domain is presented. By deliberately embedding covert signal into the primary transmitted signal, the covert signal is completely hidden, rendering conventional signal detection methods ineffective. However, the synchrosqueezing transform (SST) maps the signal into the time-frequency domain, resulting in improved time-frequency localization and more effective separation of its components. Based on the proposed instantaneous frequency (IF) curve extraction and filtered reconstruction scheme, the waveform of the covert signal can be automatically extracted. Experimental results from synthetic signal testing validate the efficiency of the proposed method, demonstrating its potential for covert communication applications.

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

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

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

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

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

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and terahertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicated a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. For hybrid beamforming structure, two approaches are introduced to obtain spatial covariance matrix in this stage. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires estimating much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the hybrid beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multi-path channels. We compare the performance of the mean square error (MSE) of channel estimation and the resulting hybrid beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings.

With the development of internet of things (IoT) technology, sensor networks are used in a wide variety of fields. However, the energy replenishment of sensor nodes (SNs) is still a challenge. In this paper, we propose an unmanned aerial vehicle (UAV) wireless charging strategy (WCS), which can jointly maximize the system profit and the number of survival nodes through an efficient charging order. To effectively reduce the data transmission delay, we group the nodes to realize the data collection solely by cluster head (CH). In the proposed charging order algorithm, we particularly design a CH replacement scheme when the original head runs out of energy before the UAV arrives. Compared with other UAV charging schemes, the proposed WCS shows a better performance in terms of system profit and time delay. Furthermore, it ensures a rather satisfying node survival rate.

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

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

With rapid advances of solar blind ultraviolet LED and ultraviolet detecting technology in recent years, ultraviolet communication gradually becomes a research hotspot due to its inherent advantages: low solar background noise, non-line-of-sight(NLOS) and good secrecy. The strong scattering characteristics in atmospheric render ultraviolet waveband the ideal choice for achieving NLOS optical communication. This paper reviews the research history and status of ultraviolet communication both in China and abroad, and especially introduces three main issues of ultraviolet communication: channel model, system analysis and design, light sources and detectors. For each aspect, current open issues and prospective research directions are analyzed.

An Efficient and flexible implementation of block ciphers is critical to achieve information security processing. Existing implementation methods such as GPP, FPGA and cryptographic application-specific ASIC provide the broad range of support. However, these methods could not achieve a good tradeoff between high-speed processing and flexibility. In this paper, we present a reconfigurable VLIW processor architecture targeted at block cipher processing, analyze basic operations and storage characteristics, and propose the multi-cluster register-file structure for block ciphers. As for the same operation element of block ciphers, we adopt reconfigurable technology for multiple cryptographic processing units and interconnection scheme. The proposed processor not only flexibly accomplishes the combination of multiple basic cryptographic operations, but also realizes dynamic configuration for cryptographic processing units. It has been implemented with 0.18µmCMOS technology, the test results show that the frequency can reach 350MHz, and power consumption is 420mw. Ten kinds of block and hash ciphers were realized in the processor. The encryption throughput of AES, DES, IDEA, and SHA-1 algorithm is 1554Mbps, 448Mbps, 785Mbps, and 424Mbps respectively, the test result shows that our processor’s encryption performance is significantly higher than other designs.

A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

Unsourced multiple access (UMA) is a multi-access technology for massive, low-power, uncoordinated, and unsourced Machine Type Communication (MTC) networks. It ensures transmission reliability under the premise of high energy efficiency. Based on the analysis of the 6G MTC key performance indicators (KPIs) and scenario characteristics, this paper summarizes its requirements for radio access networks. Following this, the existing multiple access models are analyzed under these standards to determine UMA's advantages for 6G MTC according to its design characteristics. The critical technology of UMA is the design of its multiple-access coding scheme. Therefore, the existing UMA coding schemes from different coding paradigms are further summarized and compared. In particular, this paper comprehensively considers the energy efficiency and computational complexity of these schemes, studies the changes of the above two indexes with the increase of access scale, and considers the trade-off between the two. It is revealed by the above analysis that some guiding rules of UMA coding design. Finally, the open problems and potentials in this field are given for future research.

Security and privacy issues are magnified by velocity, volume, and variety of big data. User’s privacy is an even more sensitive topic attracting most people’s attention. While XcodeGhost, a malware of iOS emerging in late 2015, leads to the privacy-leakage of a large number of users, only a few studies have examined XcodeGhost based on its source code. In this paper we describe observations by monitoring the network activities for more than 2.59 million iPhone users in a provincial area across 232 days. Our analysis reveals a number of interesting points. For example, we propose a decay model for the prevalence rate of XcodeGhost and we find that the ratio of the infected devices is more than 60%; that a lot of popular applications, such as Wechat, railway 12306, didi taxi, Youku video are also infected; and that the duration as well as the traffic volume of most XcodeGhost-related HTTP-requests is similar with usual HTTP-request which makes it difficult to be found. Besides, we propose a heuristic model based on fingerprint and its web-knowledge to identify the infected applications. The identifying result shows the efficiency of this model.

Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS (Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.

Grammar-based fuzzing is a practical and effective approach for uncovering vulnerabilities in interpreters that directly manipulate memory when parsing highly structured inputs. These fuzzers aim to generate well-formed, bug-triggering test cases using either formal grammars or ML-based techniques. To trace the development of these methods, this paper surveys 45 representative papers from top-tier conferences and Q1 journals (2007–2024), manually selected via a snowballing literature review. The survey balances foundational work and recent innovations, highlights key insights, and identifies existing solutions and gaps. It also outlines future challenges and opportunities, laying a solid foundation for continued progress in grammar-based fuzzing.

Vortex beams have gained significant attention in underwater optical wireless communication (UOWC) systems due to their unique advantages, including the ability to carry orbital angular momentum, which enables efficient spatial multiplexing for high-capacity data transmission. Their resilience to oceanic turbulence further enhances the stability of UOWC systems. Integrating fiber optic devices with optical vortex beams is crucial in this context. This paper focuses on analyzing the fiber coupling efficiency (CE) of optical vortex beams under the effects of oceanic turbulence. In the study, the extended Huygens-Fresnel principle is utilized to analyze the propagation of optical vortex waves. The necessary optical parameters for calculating fiber CE, including the optical power captured by the receiver lens and the optical power entering the fiber core, are then analytically evaluated.

Censorship circumvention technology is an indispensable part of covert communication. However, many existing censorship circumvention tools rely on Web real-time communications (WebRTC), lacking mechanisms to facilitate client-proxy circumvention channel establishment. We innovatively created EthRTC, a censorship circumvention system, utilizing blockchain as a communication channel during connection to facilitate client-broker connections. We utilize the Ethereum network as a direct medium for exchanging bootstrap information, which can be detected in the transaction pool without requiring the transaction to be included in the Ethereum consensus. This introduces an acceptable time cost during the client's bootstrap phase. During the bypass traffic transmission phase, we utilize WebRTC's DataTunnel to transfer bypass traffic, allowing users to provide proxy services for other users and making efficient use of client-side resources. We demonstrated EthRTC's effective censorship circumvention for users in censored regions in the real world. We also compared the delay between the bootstrapping phase and accessing websites during the circumvention traffic transmission with Tor's anti-censorship plugin Snowflake. EthRTC's average bootstrapping delay is around 9 seconds higher than Snowflake's 22.14 seconds. Accessing websites has nearly identical latency, showing EthRTC's acceptable introduced delay and costs.

This paper investigates the opportunistic array radar deployment problem for UAV swarms and proposes an array algorithm based on fuzzy correlation opportunistic planning. The algorithm utilizes credibility theory and comprehensively accounts for the randomness in UAV position distribution and the uncertainty in their operational states. Fuzzy variables are used to model the complex and uncertain environment of UAV deployment. A planning model for UAV opportunistic array radar deployment is developed, and an intelligent optimization algorithm is designed by integrating fuzzy simulation and non-dominated sorting genetic algorithms to solve the model. Simulation experiments confirm the effectiveness and stability of the proposed algorithm regarding monitoring coverage and power consumption under different array configurations for UAV swarm opportunistic array radar in uncertain environments, providing valuable guidance for engineering practice.

Accurate radar-cross-section (RCS) characterization of unmanned aerial vehicles (UAVs), human heads, and combined human head-hand targets is essential for integrated sensing and communication (ISAC) channel modeling. We therefore conduct monostatic RCS measurements over 0-360° azimuth in an anechoic chamber and derive statistical models for these targets. The measured RCS data are fitted using Rician, Gamma, and LogNormal distributions. Our analysis reveals that the Rician distribution is suitable for modeling small UAV RCS, while the Gamma distribution shows superiority for human head and combined human head-hand targets. Detailed distribution parameters are provided, offering valuable insights for predicting and evaluating the sensing performance of ISAC systems. These findings contribute to advancing ISAC channel research and practical applications.

Low earth orbit (LEO) constellation composed of hundreds to thousands of satellites plays a significant role in providing global low-latency and high-bandwidth internet services especially for the remote areas. However, considering the current launching capability of rocket, i.e., tens of satellites per launch, LEO constellation may consume several years or more to complete its construction. Before the constellation is constructed completely, partially constructed constellation suffers from a long-term weak connectivity, which is hardly to provide real-time end-to-end services. Fortunately, partially constructed constellation may provide non-real-time delay-tolerant services (DTSs, e.g., some earth observation and monitoring applications) through employing disruption tolerant network protocols, which could avoid the waste of satellite resources and earn early revenue to accelerate the subsequent construction of LEO constellation. To assure the service capability of DTSs in the progressive construction of constellation, we first propose a novel satellite deployment strategy with maximizing number of potential contacts (MNPC) under deployment cost constraints. Then, after some satellites are deployed successfully, a time-correlated contact plan (TCCP) scheme under time and resource constraints is proposed to maximize number of temporal transmission paths that across multiple time slots. Simulation results show that MNPC and TCCP schemes effectively optimize the delivery success ratio and delay of DTSs compared with the previous works.

Edge caching (EC) has become a promising technology that minimizes last-hop traffic by caching popular content on edge servers (ESs). Given practical resource limitation, each ES only caches a portion of the contents and cannot satisfy all user requests. How to efficiently cache the contents and route the requests is a challenging problem in EC networks. First, switching cost incurred by the caching decision is temporal-correlated. Second, since contents and requests are dynamic, each ES should continue adjusting its contents. Third, it is desirable to have an online solution with provable performance guarantee and polynomial running time. Existing efforts are insufficient to address all the above challenges. This work models the content caching and request routing of multi-ESs as a joint optimization problem, to minimize the overall system cost including storage, service and switching. We design a new online framework $JoCR$ by taking advantage of online convex optimization, predictive control, and randomized rounding methods. Theoretically, $JoCR$ can realize an excellent competitive ratio compared with the offline optimum. Practically, extensive simulations verify the efficiency of $JoCR$ over multiple benchmarks in reducing operational cost.

To address the conflict between distribution network expansion and traditional communication constraints, dual-mode communication technology that integrates high-speed power line carrier and radio frequency communication is increasingly being adopted. Efficient network analysis is essential for optimizing deployment and improving communication efficiency, scalability, and system compatibility. However, complex factors such as traffic characteristics, transmission modes, and interference avoidance policies hinder accurate evaluation of the end-to-end (E2E) delay violation probability, posing challenges for performance assessment during deployment. To tackle these challenges, this paper proposes an E2E delay analysis method based on the martingale framework. The method first constructs and analyzes delay models for different traffic types under specified network scale and node state conditions. It then transforms node states into node service capabilities, and maps the service process from the time domain to the martingale domain for analytical evaluation. In addition, we analyze the impact of non-service link traffic on the performance of service links, and derive the E2E delay violation probability based on a serial service descriptor constructed under the martingale framework. Simulation results confirm the correctness of the proposed method and demonstrate its ability to achieve a tighter delay bound.

6G demands networks that can reason, explain, and adapt in heterogeneous, fast-changing environments. Existing paradigms—rule-driven automation, template-bound IBN, and black-box KDN—struggle with scalable reasoning, interpretability, and cross-domain generalization. This paper presents knowledge-driven autonomous network (KAN), a 6G architecture that elevates cognition to a first-class capability by introducing a dedicated cognitive plane to close the loop across semantic perception, hybrid inference (forward, backward, abductive), policy synthesis, and continual learning atop a unified knowledge base. In reproducible ns-3 evaluations spanning fault recovery, cross-domain generalization, and UAV-swarm optimization, KAN consistently outperforms baselines: recovery time 2.1 s (vs. SDN 8.3 s, IBN 5.0 s, KDN-DQN 3.8 s), strategy accuracy 94.1%, and an explainable reasoning depth of $8.7$. Under domain shifts, KAN maintains an average normalized score of $0.879 \pm 0.037$ with only 10.5% degradation from the Urban baseline. In 100-UAV swarms, it achieves the highest throughput ($0.914$) and energy efficiency ($0.882$), exceeding KDN-DQN by 3.2% in throughput and 6.1% in energy efficiency, while sustaining post-recovery stability of $0.92$ and a system-level convergence time of 20.1 s. By grounding intent in symbolic knowledge and compiling it into verified, device-ready configurations, KAN advances network autonomy from opaque automation to transparent, intent-aligned cognition.

Accurate prediction of cellular network traffic is essential for efficient resource management as mobile Internet and 5G services continue to expand rapidly. However, existing methods struggle to capture complex spatiotemporal dependencies and often perform poorly on out-of-distribution data. To address these challenges, we propose a forecasting framework that leverages a pretrained large language model (LLM) and combines structured prompt engineering with time-series reprogramming. Specifically, we construct sparse neighborhood graphs using dynamic time warping (DTW) to capture long-term spatial correlations, while adaptive edge weights are employed for short-term dynamics. Furthermore, spatiotemporal priors are encoded into prompt prefixes to guide the LLM's reasoning. By reprogramming the time series data, we avoid the need for extensive model fine-tuning. Experiments on a real-world dataset demonstrate that our method outperforms classical and recent graph-based baselines by reducing prediction error by over 10% on average, and that each component of the framework contributes to the overall performance. Notably, it exhibits strong zero-shot generalization capability in unseen regions, offering a robust and scalable solution for future networks.

Contention resolution diversity slotted aloha (CRDSA) can offer an attractive way to promote the system throughput of the prevailing satellite-oriented internet of things (IoT) networks. In this work, energy efficiency of the CRDSA scheme based on non-orthogonal multiple access (NOMA), namely CRDSA-NOMA, is investigated for satellite IoT scenarios with battery-operated user equipments. Under the statistical hypothesis on the received power level (RePL), the transmission success rate of CRDSA-NOMA is first derived, and an energy-efficient CRDSA-NOMA scheme is then proposed by solving the formulated optimization problem, where the optimal probability distribution of RePL is obtained by leveraging the concept of differential evolution. After that, the upper bound of the achievable energy efficiency is presented through asymptotic analysis. Additionally, a load back-off (LBO) is integrated with the proposed scheme to handle the improper load deviation induced by the inaccurate analytical results. A candidate implementation of the proposed scheme is finally provided in the IoT system using DVB-RCS2 standard, where a lookup table is built to minimize the signaling overhead. Numerical simulation results illustrate the effectiveness of the proposed scheme, which experiences more elastic behaviors in terms of energy efficiency, throughput, and PLR, as compared to the other existing schemes.

With the adoption of multi-band transmission in optical networks, fully upgrading all network links can lead to underutilization of spectrum resources on certain links due to constraints such as spectrum continuity. To address this issue, selectively upgrading only a subset of links to support multi-band transmission has emerged as a cost-effective alternative. However, whether such a partially link-upgraded approach can achieve comparable performance to the network with fully band-upgraded links remains an open and urgent question. For this, we propose the systematic analysis of partially link-upgraded multi-band networks. We first introduce different link upgrade methods to determine which links should be upgraded and propose a novel inter-band wavelength conversion strategy designed to optimize signal transmission across partially band-upgraded links. To evaluate and compare the performance of this strategy with alternative approaches including fully link-upgraded networks, we introduce a generalized signal-to-noise ratio (GSNR) estimation model under wavelength conversion and a network cost model. We then propose heuristic algorithms for different wavelength conversion strategies for lightpath establishment in the multi-band optical networks. Simulation results show that, the stage-based proportional link upgrade method enables rapid capacity expansion while effectively controlling the upgrade cost. In the partially link-upgraded network, the proposed inter-band wavelength conversion strategies can not only significantly enhance the network capacity, but also significantly reduce the network upgrade cost, thus achieving an effective trade-off between capacity enhancement and cost reduction. Moreover, according to the link upgrade proportions, the partial-band upgrades are particularly suitable for large and sparse networks.

A highly efficient Doherty power amplifier (DPA) with a new band-pass power divider for better performance in wide frequency ranges has been introduced. The proposed DPA is a combination of a Class-F carrier amplifier utilizing Class-AB bias and a Class-F peaking amplifier with Class-C bias to allow for amplification with the highest efficiency, at levels of low-power and high-power. A noteworthy feature of the design is the bandpass power divider designed with interdigital structures and asymmetrical low-impedance lines. It removes DC-block capacitors, lowers insertion loss, and improves harmonic suppression. For the class-F power amplifiers, a new harmonic control circuit (HCC) has been introduced, which is designed based on the transistor's output capacitor and in the resonance conditions. The DPA was fabricated, with a maximum drain efficiency (DE) more than 69.26%, a 10.23 dB power gain and a $P_{\mathrm{out}}$ of 29.23 dBm. These advances in power efficiency and bandwidth make the DPA highly conducive to sub-6 GHz 5G NR Band n41 (2.496—2.690 GHz) and LTE Bands 7 and 41. High PAPR signals above 69% drain efficiency and 500 MHz bandwidth are supported by the DPA's design, which is in line with the requirements of these standards.

The low-altitude intelligent network (LAIN) is gaining significant attention from academia and industry. Fortunately, LAIN naturally aligns with near-field communications in extremely large-scale MIMO (XL-MIMO) systems. By leveraging near-field beamfocusing, LAIN can precisely direct beam energy to unmanned aerial vehicles, while the additional distance dimension boosts overall spectrum efficiency. However, near-field communications in LAIN still face several challenges, such as the increase in signal processing complexity and the necessity of distinguishing between far and near-field users. Inspired by the large language models (LLM) with powerful ability to handle complex problems, we apply LLM to solve challenges of near-field communications in LAIN. The objective of this article is to provide a comprehensive analysis and discussion on LLM-empowered near-field communications in LAIN. Specifically, we first introduce fundamentals of LLM and near-field communications, including the key advantages of LLM and key characteristics of near-field communications. Then, we reveal the opportunities and challenges of near-field communications in LAIN. To address these challenges, we present a LLM-based scheme for near-field communications in LAIN, and provide a case study which jointly distinguishes far and near-field users and designs multi-user precoding matrix. Finally, we outline and highlight several future research directions and open issues.

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature. VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth. Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources. The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.