As Satellite Frequency and Orbit (SFO) constitute scarce natural resources, constructing a Satellite Frequency and Orbit Knowledge Graph (SFO-KG) becomes crucial for optimizing their utilization. In the process of building the SFO-KG from Chinese unstructured data, extracting Chinese entity relations is the fundamental step. Although Relation Extraction (RE) methods in the English field have been extensively studied and developed earlier than their Chinese counterparts, their direct application to Chinese texts faces significant challenges due to linguistic distinctions such as unique grammar, pictographic characters, and prevalent polysemy. The absence of comprehensive reviews on Chinese RE research progress necessitates a systematic investigation. A thorough review of Chinese RE has been conducted from four methodological approaches: pipeline RE, joint entity- relation extraction, open domain RE, and multimodal RE techniques. In addition, we further analyze the essential research infrastructure, including specialized datasets, evaluation benchmarks, and competitions within Chinese RE research. Finally, the current research challenges and development trends in the field of Chinese RE were summarized and analyzed from the perspectives of ecological construction methods for datasets, open domain RE, N-ary RE, and RE based on large language models. This comprehensive review aims to facilitate SFO-KG construction and its practical applications in SFO resource management.

The emerging deployment of large-scale Low Earth Orbit (LEO) satellite constellations provides seamless global coverage. However, the increasing number of satellites also introduces significant security challenges, such as eavesdropping and illegal communication behavior detection. This paper investigates covert wireless communication over uplink satellite-terrestrial network, focusing on scenarios with warden satellites. By accounting for shot noise generated by ambient signals from terrestrial interferers, the terrestrial transmitter Alice can effectively hide its signal from warden satellites. Leveraging stochastic geometry, the distributions of distances between transmitter and satellites are analyzed, enabling the assessment of uplink performance and interference within a satellite’s coverage area. Approximate expressions for detection error probability and transmission outage probability are derived. Based on the theoretical analysis, an optimal scheme is proposed to maximize covert throughput under the constraint of the average detection error probability of the most detrimental warden satellite. Extensive Monte Carlo simulations experiments are conducted to validate the accuracy of analytical methods for evaluating covert performance.

Terahertz (THz) and millimeter Wave (mmWave) have been considered as potential frequency bands for 6G cellular systems to meet the need of ultra-high data rates. However, indoor communications could be blocked in THz/mmW cellular systems due to the high free-space propagation loss. Deploying a large number of small base stations indoors has been considered as a promising solution for solving indoor coverage problems. However, base station dense deployment leads to a significant increase in system energy consumption. In this paper, we develop a novel ultra-efficient energy-saving mechanism with the aim of reducing energy consumption in 6G distributed indoor base station scenarios. Unlike the existing relevant protocol framework of 3GPP, which operates the cellular system based on constant system signaling messages (including cell ID, cell reselection information, etc.), the proposed mechanism eliminates the need for system messages. The intuition comes from the observation that the probability of having no users within the coverage area of an indoor base station is high, hence continuously sending system messages to guarantee the quality of service is unnecessary in indoor scenarios. Specifically, we design a dedicated beacon signal to detect whether there are users in the coverage area of the base station and switch off the main communication module when there are no active users for energy saving. The beacon frame structure is carefully designed based on the existing 3GPP specifications with minimal protocol modifications, and the protocol parameters involved are optimized. Simulation results show that the proposed mechanism can reduce the system energy from the order of tens of watts to the order of hundreds of milliwatts. Compared to traditional energy-saving schemes, the proposed mechanism achieves an average energy-saving gain of 58%, with a peak energy-saving gain of 90%.

The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things (IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.

The increasing demand for infotainment applications necessitates efficient bandwidth and energy resource allocation. Sixth-Generation (6G) networks, utilizing Cognitive Radio (CR) technology within CR Network (CRN), can enhance spectrum utilization by accessing unused spectrum when licensed Primary Mobile Equipment (PME) is inactive or served by a Primary Base Station (PrBS). Secondary Mobile Equipment (SME) accesses this spectrum through a Secondary Base Station (SrBS) using opportunistic access, i.e., spectrum sensing. Hybrid Multiple Access (HMA), combining Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), can enhance Energy Efficiency (EE). Additionally, SME Clustering (SMEC) reduces inter-cluster interference, enhancing EE further. Despite these advancements, the integration of CR technology, HMA, and SMEC in CRN for better bandwidth utilization and EE remains unexplored. This paper introduces a new CR- assisted SMEC-based Downlink HMA (CR-SMEC-DHMA) method for 6G CRN, aimed at jointly optimizing SME admission, SME association, sum rate, and EE subject to imperfect sensing, collision, and Quality of Service (QoS). A novel optimization problem, formulated as a non-linear fractional programming problem, is solved using the Charnes-Cooper Transformation (CCT) to convert into a concave optimization problem, and an 𝜖-optimal Outer Approximation Algorithm (OAA) is employed to solve the concave optimization problem. Simulations demonstrate the effectiveness of the proposed CR-SMEC-DHMA, surpassing the performance of current OMA- enabled CRN, NOMA-enabled CRN, SMEC-OMA enabled CRN, and SMEC-NOMA enabled CRN methods, with 𝜖-optimal results obtained at 𝜖 = 10−3, while satisfying Performance Measures (PMs) including SME admission in SMEC, SME association with SrBS, SME-channel opportunistic allocation through spectrum sensing, sum rate and overall EE within the 6G CRN.

The Unmanned Aerial Vehicle (UAV)-assisted sensing-transmission-computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure. To tackle the challenges of data transmission and enable timely rescue decision-making, we propose DWT-3DRec-an efficient wireless transmission model for 3D scene reconstruction. This model leverages MobileNetV2 to extract image and pose features, which are transmitted through a Dual-path Adaptive Noise Modulation network (DANM). Moreover, we introduce the Gumbel Channel Masking Module (GCMM), which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise. At the ground receiver, the Multi-scale Deep Source-Channel Coding for 3D Reconstruction (MDS-3DRecon) framework integrates Deep Joint Source-Channel Coding (DeepJSCC) with Cityscale Neural Radiance Fields (CityNeRF). It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module (AFM) to achieve high-precision scene reconstruction. Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group (JPEG) standard in transmitting image and pose data, achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio (SNR) range of 5-20 dB. In large-scale 3D scene reconstruction tasks, MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields (Mip-NeRF) and Bungee Neural Radiance Field (BungeeNeRF), achieving a Peak Signal-to-Noise Ratio (PSNR) of 24.921 dB and a reconstruction loss of 0.188. Ablation studies further confirm the essential roles of GCMM, DANM, and AFM in enabling high-fidelity 3D reconstruction.

Traceability and trustiness are two critical issues in the logistics sector. Blockchain provides a potential way for logistics tracking systems due to its traits of tamper resistance. However, it is non-trivial to apply blockchain on logistics because of firstly, the binding relationship between virtue data and physical location cannot be guar- anteed so that frauds may exist. Secondly, it is neither practical to upload complete data on the blockchain due to the limited storage resources nor convincing to trust the digest of the data. This paper proposes a traceable and trustable consortium blockchain for logistics T²L to provide an efficient solution to the mentioned problems. Specifically, the authenticated geocoding data from telecom operators’ base stations are adopted to ensure the location credibility of the data before being uploaded to the blockchain for the purpose of reliable traceability of the logistics. Moreover, we propose a scheme based on Zero Knowledge Proof of Retrievability (ZK BLS-PoR) to ensure the trustiness of the data digest and the proofs to the blockchain. Any user in the system can check the data completeness by verifying the proofs instead of downloading and examining the whole data based on the pro- posed ZK BLS- PoR scheme, which can provide solid theoretical verification. In all, the proposed T²L framework is a traceable and trustable logistics system with a high level of security.

The rapid growth of blockchain and Decentralized Finance (DeFi) has introduced new challenges and vulnerabilities that threaten the integrity and efficiency of the ecosystem. This study identifies critical issues such as Transaction Order Dependence (TOD), Blockchain Extractable Value (BEV), and Transaction Importance Diversity (TID), which collectively undermine the fairness and security of DeFi systems. BEV-related activities, including sandwich attacks, liquidations, transaction replay etc. have emerged as significant threats, collectively generating $540.54 million in losses over 32 months across 11,289 addresses, involving 49,691 cryptocurrencies and 60,830 on-chain markets. These attacks exploit transaction mechanics to manipulate asset prices and extract value at the expense of other participants, with sandwich attacks being particularly impactful. Additionally, the growing adoption of blockchain in traditional finance highlights the challenge of TID, wherein high transaction volumes can strain systems and compromise time-sensitive operations. To address these pressing issues, we propose a novel Distributed Transaction Sequencing Strategy (DTSS) that integrates forking mechanisms with an Analytic Hierarchy Process (AHP) to enforce fair and transparent transaction ordering in a decentralized manner. Our approach is further enhanced by an optimization framework and the introduction of a Normalized Allocation Disparity Metric (NADM) that ensures optimal parameter selection for transaction prioritization. Experimental evaluations demonstrated that the DTSS effectively mitigated BEV risks, enhanced transaction fairness, and significantly improved the security and transparency of DeFi ecosystems.

Vehicular Ad-hoc Network (VANET) is a platform that facilitates Vehicle-to-Everything (V2X) interconnection. However, its open communication channels and high-speed mobility introduce security and privacy vulnerabil- ities. Anonymous authentication is crucial in ensuring secure communication and privacy protection in VANET. However, existing anonymous authentication schemes are prone to single points of failure and often overlook the efficient tracking of the true identities of malicious vehicles after pseudonym changes. To address these chal- lenges, we propose an efficient anonymous authentication scheme for blockchain-based VANET. By leveraging blockchain technology, our approach addresses the challenges of single points of failure and high latency, thereby enhancing the service stability and scalability of VANET. The scheme integrates homomorphic encryption and elliptic curve cryptography, allowing vehicles to independently generate new pseudonyms when entering a new domain without third-party assistance. Security analyses and simulation results demonstrate that our scheme achieves effective anonymous authentication in VANET. Moreover, the roadside unit can process 500 messages per 19 ms. As the number of vehicles in the communication domain grows, our scheme exhibits superior message- processing capabilities.

When deploying Reconfigurable Intelligent Surface (RIS) to improve System Sum-Rate (SSR), the timeliness and accuracy of SSR optimization methods are difficult to achieve simultaneously through a single algorithm. Some algorithms focus on timeliness, while some focus on accuracy. In this paper, in order to take into account the timeliness and accuracy of the system comprehensively, we construct SSR analysis model of RIS-assisted multi- user downlink communication system and propose several new optimization methods. The goal is to maximize SSR by using the proposed algorithms to jointly optimize power allocation and reflection coefficients. To solve this comprehensive problem, two sets of Alternating Optimization (AO)-based timeliness algorithms and one set of Monotonic Optimization (MO)-based accuracy algorithms are proposed separately to jointly optimize system performance. First, the Water-Filling (WF)-based and penalty-based low complexity algorithms are developed to optimize power allocation and reflection coefficients respectively. To improve the reality of the calculation, penalty-based algorithm cleverly considers residual noise that is difficult to calculate. Then, for further improve the timeliness, a new Successive Convex Approximation (SCA)-based low complexity algorithm is designed to further optimize reflection coefficients and its convergence is proved. Third, in order to verify the effectiveness of the proposed timeliness algorithms, we further propose MO-based accuracy algorithms, in which, the Polyblock Outer Approximation (POA) algorithm, the Semidefinite Relaxation (SDR) method, and the bisection search algorithm are combined in a novel way. Numerical results confirm the timeliness of AO-based algorithms and the accuracy of MO-based algorithms. They supervise and complement each other.

A Reconfigurable Intelligent Surface (RIS) can relay signals from the transmitter to the receiver. In this regard, RISs operate similarly to traditional relays. We design a Multiple-Input-Multiple-Output (MIMO) system with a hybrid network of RIS and Half-Duplex (HD) Amplify-and-Forward (AF) relay. We model the system’s signal propagation and propose a new algorithm to get the system’s Achievable Rate (AR) value. We complete simulations to evaluate the performance of the RIS and HD-AF relay hybrid network system compared to the system assisted by either the RIS or HD-AF relay. The simulations indicate that many factors can considerably influence the system performance. Selecting an optimal placement for the RIS and relay can result in the best performance for the RIS and HD-AF relay hybrid network system in situations where the direct link between the receiver and transmitter is absent.

Increasing reliance on large-scale AI models has led to rising demand for intelligent services. The centralized cloud computing approach has limitations in terms of data transfer efficiency and response time, and as a result many service providers have begun to deploy edge servers to cache intelligent services in order to reduce transmission delay and communication energy consumption. However, finding the optimal service caching strategy remains a significant challenge due to the stochastic nature of service requests and the bulky nature of intelligent services. To deal with this, we propose a distributed service caching scheme integrating deep reinforcement learning (DRL) with mobility prediction, which we refer to as DSDM. Specifically, we employ the D3QN (Deep Double Dueling Q-Network) framework to integrate Long Short-Term Memory (LSTM) predicted mobile device locations into the service caching replacement algorithm and adopt the distributed multi-agent approach for learning and training. Experimental results demonstrate that DSDM achieves significant performance improvements in reducing communication energy consumption compared to traditional methods across various scenarios.

Spatial-temporal traffic prediction technology is crucial for network planning, resource allocation optimizing, and user experience improving. With the development of virtual network operators, multi-operator collaborations, and edge computing, spatial-temporal traffic data has taken on a distributed nature. Consequently, non- centralized spatial-temporal traffic prediction solutions have emerged as a recent research focus. Currently, the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station. This method reduces additional burden on communication systems. However, this method has a drawback: it cannot handle irregular traffic data. Due to unstable wireless network environments, device failures, insufficient storage resources, etc., data missing inevitably occurs during the process of collecting traffic data. This results in the irregular nature of distributed traffic data. Yet, commonly used traffic prediction models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) typically assume that the data is complete and regular. To address the challenge of handling irregular traffic data, this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic. To solve the aforementioned problems, this paper introduces split learning to design a structured distributed learning framework. The framework comprises a Global-level Spatial structure mining Model (GSM) and several Node- level Generative Models (NGMs). NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller. Firstly, the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables. Secondly, GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data. Finally, NGM generates future traffic based on latent temporal and spatial feature variables. The introduction of the time attention mechanism enhances the framework’s capability to handle irregular traffic data. Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction, which compensates for missing information in local irregular traffic data. The proposed framework effectively addresses the distributed prediction issues of irregular traffic data. By testing on real world datasets, the proposed framework improves traffic prediction accuracy by 35% compared to other commonly used distributed traffic prediction methods.

To overcome the challenges of poor real-time performance, limited scalability, and low intelligence in conventional jamming pattern recognition methods, this paper proposes a method based on Wavelet Packet Decomposition (WPD) and enhanced deep learning techniques. In the proposed method, an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall (SW), which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network. The network employs a bilateral filter to preprocess the input SW, thereby enhancing the edge features of the jamming signals. To extract abstract features, depthwise separable convolution is utilized instead of traditional convolution, thereby reducing the network’s parameter count and enhancing real-time performance. A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes, thus enhancing scalability. During network training, adaptive moment estimation is employed as the optimizer, allowing the network to dynamically adjust the learning rate and accelerate convergence. A comprehensive comparison between the proposed jamming recognition network and six other models is conducted, along with Ablation Experiments (AE) based on numerical simulations. Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy, network complexity, and prediction time.

In the big data era, the surge in network traffic volume poses challenges for network management and cyberse- curity. Network Traffic Classification (NTC) employs deep learning to categorize traffic data, aiding security and analysis systems as Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). However, current NTC methods, based on isolated network simulations, usually fail to adapt to new protocols and applications and ignore the effects of network conditions and user behavior on traffic patterns. To improve network traffic management insights, federated learning frameworks have been proposed to aggregate diverse traffic data for collaborative model training. This approach faces challenges like data integrity, label noise, packet loss, and skewed data distributions. While label noise can be mitigated through the use of sophisticated traffic labeling tools, other issues such as packet loss and skewed data distributions encountered in Network Packet Brokers (NPB) can severely impede the efficacy of federated learning algorithms. In this paper, we introduced the Robust Traffic Classifier with Federated Contrastive Learning (FC-RTC), combining federated and contrastive learning methods. Using the Supcon-Loss function from contrastive learning, FC-RTC distinguishes between similar and dissimilar samples. Training by sample pairs, FC-RTC effectively updates when receiving corrupted traffic data with packet loss or disorder. In cases of sample imbalance, contrastive loss functions for similar samples reduce model bias towards higher proportion data. By addressing uneven data distribution and packet loss, our system enhances its capability to adapt and perform accurately in real-world network traffic analysis, meeting the spe- cific demands of this complex field.

Precodings using square-root decomposition, including Cholesky and G-To-Minus-Half (GTMH) precodings, are promising for eliminating the Faster-Than-Nyquist (FTN)-induced Intersymbol-Interference (ISI). However, the existing precodings using square-root decomposition either ignore Interblock-Interference (IBI) or increase the signal power, deteriorating the Bit Error Rate (BER) performance for high-order modulations and severe ISI. To overcome these drawbacks, we adopt two approaches for constructing the circular ISI matrix. The first approach inserts a Cyclic Prefix/Suffix (CPS) after each precoded symbol block, while the second approach replaces the linear convolution of the FTN shaping and the matched filter by the circular convolution, resulting in the Circular FTN (CFTN). Using these two approaches, we propose three IBI-free precodings, i.e., CPS-Cholesky, CFTN-Cholesky and CFTN-GTMH precodings. Furthermore, employing QR decomposition shows that the GTMH and Cholesky precodings can be converted interchangeably. Thus, we demonstrate that the GTMH precoding is essentially equivalent to the Cholesky precoding. Simulation results indicate that the BER performance of three IBI-free precodings approaches Nyquist performance for moderate ISI. However, as ISI intensifies, the CPS-Cholesky scheme increases the transmit power, causing BER performance degradation. In contrast, the CFTN-Cholesky and CFTN-GTMH precodings maintain optimal BER performance even for severe ISI. Considering 128-amplitude phase shift keying with a code rate of 1/2, the BER loss of CFTN-Cholesky and CFTN-GTMH precodings for the ideal BER of 10-5 is approximately 0.002 dB and 0.005 dB when packing factor is 0.7 and roll-off factor is 0.3. To the best knowledge of the authors, this is the optimal performance achievable through precoding.

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today’s modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult to successfully combat spam attacks. In this paper, we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection. We use a benchmark SMS spam dataset to analyze this spam detection model. Additionally, we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques. The overall experiment showed that our optimized fine-tuned BERT (Bidirectional Encoder Representations from Transformers) variant model RoBERTa obtained high accuracy with 99.84%. To further enhance model transparency, we incorporate Explainable Artificial Intelligence (XAI) techniques that compute positive and negative coefficient scores, offering insight into the model’s decision-making process. Additionally, we evaluate the performance of traditional machine learning models as a baseline for comparison. This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.

In the rapidly evolving landscape of Industry 4.0 (I4.0), the convergence of information and operational technologies necessitates real-time communication and collaboration across cyber-physical systems and the Internet of Things (IoT). Rapid data transmission is particularly critical within enterprises (vertically) and among stakeholders (horizontally) in this complex, heterogeneous ecosystem. While current research has focused on data application, processing, and storage within the cloud-edge-device continuum, cross-edge transmission has received less attention, resulting in challenges such as suboptimal routing and excessive delays in horizontal communications. To address the above issues, this paper introduces a Connection-As-Required Scheme (CARS) specifically designed for delay-sensitive IoT and Cyber-Physical System (CPS) applications, where low-latency communication is essential for operational efficiency. CARS leverages Lyapunov optimization and backpressure algorithms to optimize traffic scheduling and routing, minimizing communication delay between entities. Benchmarking against state-of-the-art solutions, CARS reduces Round-Trip Time (RTT) to approximately 47.0% of conventional methods and decreases delay by 24.5% in TCP-based and 26.0% in UDP-based applications. These results highlight the potential of CARS to facilitate effective, low-latency collaboration in diverse I4.0 environments.

Vehicular Edge Computing (VEC) enhances the quality of user services by deploying wealth of resources near vehicles. However, due to highly dynamic and complex nature of vehicular networks, centralized decision- making for resource allocation proves inadequate within VECs. Conversely, allocating resources via distributed decision-making consumes vehicular resources. To improve the quality of user service, we formulate a problem of latency minimization, further subdividing this problem into two subproblems to be solved through distributed decision-making. To mitigate the resource consumption caused by distributed decision-making, we propose Reinforcement Learning (RL) algorithm based on sequential alternating multi-agent system mechanism, which effectively reduces the dimensionality of action space without losing the informational content of action, achieving network lightweighting. We discuss the rationality, generalizability, and inherent advantages of proposed mechanism. Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability, generalizability, and adaptability to scenarios with invalid actions, all while achieving network lightweighting.

During indoor operations, Unmanned Aerial Vehicles (UAVs) are required to embody attributes such as heightened sensitivity, compact design, and robust maneuverability. A high operational advantage is evident when tasks are executed using multiple UAVs in unison. Despite the prevalent focus in current UAV research on enhancing discrete components or modules, a holistic, integrated approach that encompasses the UAV architecture, platform design, algorithms, simulation, and swarm intelligence, is lacking. This study introduces a micro-UAV swarm system designed for efficient perception within partially known indoor environments. We devised the comprehensive architectural blueprint of a micro-UAV swarm system. A communication routing evaluation metric is proposed to improve the quality of intercommunication among UAVs in the micro-UAV swarm. In addressing the localization and perception challenges, this study features the development of a multisensor-based autonomous positioning methodology, complemented by an object detection and tracking framework based on YOLOv5 and DeepSORT technologies. In the realm of decision making, we used the DuelingDQN algorithm to facilitate mission allocation and scheduling within the micro-UAV swarm system. For flight control, we introduced a control strategy that integrated pipeline control and visual servoing mechanisms. We developed a dedicated simulation platform and designed a realistic scenario to rigorously validate the efficacy of the entire micro-UAV swarm system in simulated exercises and actual flight tests.

Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas. By overcoming the limitations of traditional terrestrial communication networks, it enables long-distance data transmission anytime and anywhere, ensuring the timely and accurate delivery of water level data, which is particularly crucial for fishway water level monitoring. To enhance the effectiveness of fishway water level monitoring, this study proposes a multi-task learning model, AS-SOMTF, designed for real-time and comprehensive prediction. The model integrates auxiliary sequences with primary input sequences to capture complex relationships and dependencies, thereby improving representational capacity. In addition, a novel time- series embedding algorithm, AS-SOM, is introduced, which combines generative inference and pooling operations to optimize prediction efficiency for long sequences. This innovation not only ensures the timely transmission of water level data but also enhances the accuracy of real-time monitoring. Compared with traditional models such as Transformer and Long Short-Term Memory (LSTM) networks, the proposed model achieves improvements of 3.8% and 1.4% in prediction accuracy, respectively. These advancements provide more precise technical support for water level forecasting and resource management in the Diqing Tibetan Autonomous Prefecture of the Lancang River, contributing to ecosystem protection and improved operational safety.

Speech Emotion Recognition (SER) has received widespread attention as a crucial way for understanding human emotional states. However, the impact of irrelevant information on speech signals and data sparsity limit the development of SER system. To address these issues, this paper proposes a framework that incorporates the Attentive Mask Residual Network (AM-ResNet) and the self-supervised learning model Wav2vec 2.0 to obtain AM-ResNet features and Wav2vec 2.0 features respectively, together with a cross-attention module to interact and fuse these two features. The AM-ResNet branch mainly consists of maximum amplitude difference detection, mask residual block, and an attention mechanism. Among them, the maximum amplitude difference detection and the mask residual block act on the pre-processing and the network, respectively, to reduce the impact of silent frames, and the attention mechanism assigns different weights to unvoiced and voiced speech to reduce redundant emotional information caused by unvoiced speech. In the Wav2vec 2.0 branch, this model is introduced as a feature extractor to obtain general speech features (Wav2vec 2.0 features) through pre-training with a large amount of unlabeled speech data, which can assist the SER task and cope with data sparsity problems. In the cross-attention module, AM-ResNet features and Wav2vec 2.0 features are interacted with and fused to obtain the cross-fused features, which are used to predict the final emotion. Furthermore, multi-label learning is also used to add ambiguous emotion utterances to deal with data limitations. Finally, experimental results illustrate the usefulness and superiority of our proposed framework over existing state-of-the-art approaches.

Edge computing is swiftly gaining traction and is being standardised by the European Telecommunications Standards Institute (ETSI) as Multi-access Edge Computing (MEC). Simultaneously, oneM2M has been actively developing standards for dynamic data management and IoT services at the edge, particularly for applications that require real-time support and security. Integrating MEC and oneM2M offers a unique opportunity to maximize their individual strengths. Therefore, this article proposes a framework that integrates MEC and oneM2M standard platforms for IoT applications, demonstrating how the synergy of these architectures can leverage the geographically distributed computing resources at base stations, enabling efficient deployment and added value for time-sensitive IoT applications. In addition, this study offers a concept of potential interworking models between oneM2M and the MEC architectures. The adoption of these standard architectures can enable various IoT edge services, such as smart city mobility and real-time analytics functions, by leveraging the oneM2M common service layer instantiated on the MEC host.

As key technologies in 6G, Space-Air-Ground Integrated Networks (SAGIN) promises to provide seamless global coverage through a comprehensive, ubiquitous communication system, while Integrated Sensing and Communications (ISAC) effectively addresses spectrum congestion by sharing spectrum resources and transceivers for simultaneous communication and sensing operations. However, existing ISAC research has primarily focused on terrestrial networks, with limited exploration of its applications in SAGIN environments. This paper proposes a novel SAGIN-ISAC scheme leveraging High-Altitude Platform Stations (HAPS). In this scheme, HAPS serves as a relay node that not only amplifies and forwards communication signals but also receives and processes target echo signals for parameter estimation. The satellite employs Resilient Massive Access (RMA) to provide communication services to different User Terminals (UTs). To address scenarios with an unknown number of targets, we develop a Two-threshold Detection and Parameter Multiple Signal Classification (MUSIC) algorithm (TDPM), which employs dual-threshold correlation detection to determine the number of targets and utilizes the MUSIC algorithm to estimate targets’ Angle of Arrival (AoA), range, and relative velocity. Furthermore, we establish a joint optimization framework that considers both communication and sensing performance, optimizing energy efficiency, detection probability, and the Cramér-Rao bound. The power allocation coefficients are derived through Nash equilibrium, while the precoding matrix is optimized using Sequential Convex Approximation (SCA) to address the non-convex nature of the optimization problem. Experimental results demonstrate that our proposed scheme significantly enhances the overall performance of the SAGIN-ISAC system.

Integrated Sensing and Communication (ISAC) is considered a key technology in 6G networks. An accurate sensing channel model is crucial for the design and sensing performance evaluation of ISAC systems. The widely used Geometry-Based Stochastic Model (GBSM), typically applied in standardized channel modeling, mainly focuses on the statistical fading characteristics of the channel. However, it fails to capture the characteristics of targets in ISAC systems, such as their positions and velocities, as well as the impact of the targets on the background. To address this issue, this paper proposes an Extended-GBSM (E-GBSM) sensing channel model that incorporates newly discovered channel characteristics into a unified modeling framework. In this framework, the sensing channel is divided into target and background channels. For the target channel, the model introduces a concatenated modeling approach, while for the background channel, a parameter called the power control factor is introduced to assess impact of the target on the background channel, making the modeling framework applicable to both mono-static and bi-static sensing modes. To validate the proposed model’s effectiveness, measurements of target and background channels are conducted across a wide range of indoor and outdoor scenarios, covering various sensing targets such as metal plates, reconfigurable intelligent surfaces, human bodies, unmanned aerial vehicles, and vehicles. The experimental results provide important theoretical support and empirical data for the standardization of ISAC channel modeling.

This paper proposes the Unmanned Aerial Vehicle (UAV)-assisted Full-Duplex (FD) Integrated Sensing And Communication (ISAC) system. In this system, the UAV integrates sensing and communication functions, capable of receiving transmission signals from Uplink (UL) users and echo signal from target, while communicating with Downlink (DL) users and simultaneously detecting target. With the objective of maximizing the Average Sum Rate (ASR) for both UL and DL users, a composite non-convex optimization problem is established, which is decomposed into sub-problems of communication scheduling optimization, transceiver beamforming design, and UAV trajectory optimization. An alternating iterative algorithm is proposed, employing relaxation optimization, extremum traversal search, augmented weighted minimum mean square error, and successive convex approximation methods to solve the aforementioned sub-problems. Simulation results demonstrate that, compared to the traditional UAV-assisted Half-Duplex (HD) ISAC scheme, the proposed FD ISAC scheme effectively improves the ASR.

We introduce a pioneering anomaly detection framework within spatial crowdsourcing Internet of Drone Things (IoDT), specifically designed to improve bushfire management in Australia’s expanding urban areas. This framework innovatively combines Graph Neural Networks (GNN) and advanced data fusion techniques to enhance IoDT capabilities. Through spatial crowdsourcing, drones collectively gather diverse, real-time data across multiple locations, creating a rich dataset for analysis. This method integrates spatial, temporal, and various data modalities, facilitating early bushfire detection by identifying subtle environmental and operational changes. Utilizing a complex GNN architecture, our model effectively processes the intricacies of spatially crowdsourced data, significantly increasing anomaly detection accuracy. It incorporates modules for temporal pattern recognition and spatial analysis of environmental impacts, leveraging multimodal data to detect a wide range of anomalies, from temperature shifts to humidity variations. Our approach has been empirically validated, achieving an F1 score of 0.885, highlighting its superior anomaly detection performance. This integration of spatial crowdsourcing with IoDT not only establishes a new standard for environmental monitoring but also contributes significantly to disaster management and urban sustainability.

Sixth Generation (6G) mobile communication networks will involve sensing as a new function, with the overwhelming trend of Integrated Sensing And Communications (ISAC). Although expanding the serving range of the networks, there exists performance trade-off between communication and sensing, in that they have competitions on the physical resources. Different resource allocation schemes will result in different sensing and communication performance, thus influencing the system’s overall performance. Therefore, how to model the system’s overall performance, and how to optimize it are key issues for ISAC. Relying on the large-scale deployment of the networks, cooperative ISAC has the advantages of wider coverage, more robust performance and good compatibility of multiple monostatic and multistatic sensing, compared to the non-cooperative ISAC. How to capture the performance gain of cooperation is a key issue for cooperative ISAC. To address the aforementioned vital problems, in this paper, we analyze the sensing accuracy gain, propose a unified ISAC performance evaluation framework and design several optimization methods in cooperative ISAC systems. The cooperative sensing accuracy gain is theoretically analyzed via Cramér Rao lower bound. The unified ISAC performance evaluation model is established by converting the communication mutual information to the effective minimum mean squared error. To optimize the unified ISAC performance, we design the optimization algorithms considering three factors: base stations’ working modes, power allocation schemes and waveform design. Through simulations, we show the performance gain of the cooperative ISAC system and the effectiveness of the proposed optimization methods.

Integrated Sensing And Communication (ISAC) is regarded as a promising technology for facilitating the rapid advancement of Sixth-Generation (6G) due to its concurrent transmission of information and environmental sensing capabilities. Rate-Splitting Multiple Access (RSMA), through the utilization of Successive Interference Cancellation (SIC) and Rate-Splitting (RS) at the transceiver, can fulfill the sensing requirement and supersede individual radar sequence to mitigate the interference between communication and sensing. This paper investigates the transceiver design of the RSMA-assisted ISAC in a Network-Assisted Full-Duplex (NAFD) cell-free Massive Multiple-Input Multiple-Output (mMIMO) system. We first derive the expressions of the communication achievable data rate and radar sensing Signal to Interference plus Noise Ratio (SINR). Subsequently, an optimization problem is formulated to maximize the communication achievable data rate, subject to both radar sensing constraints and fronthaul constraints, an effective algorithm based on sparse beamforming scheme and Semi-Definite Relaxation (SDR) is then proposed to acquire the near-optimal transceiver. Numerical results demonstrate that the application of RSMA technology in ISAC systems can significantly enhance system performance, and reveal that Dual-Functionalities Radar-Communication (DFRC) scheme can achieve higher data rate than the traditional scheme.

As the 5G architecture gains momentum, interest in 6G is growing. The proliferation of Internet of Things (IoT) devices, capable of capturing sensitive images, has increased the need for secure transmission and robust access control mechanisms. The vast amount of data generated by low-computing devices poses a challenge to traditional centralized access control, which relies on trusted third parties and complex computations, resulting in intricate interactions, higher hardware costs, and processing delays. To address these issues, this paper introduces a novel distributed access control approach that integrates a decentralized and lightweight encryption mechanism with image transmission. This method enhances data security and resource efficiency without imposing heavy computational and network burdens. In comparison to the best existing approach, it achieves a 7% improvement in accuracy, effectively addressing existing gaps in lightweight encryption and recognition performance.