Large AI models have been widely adopted in wireless communications for channel modeling, beamforming, and resource optimization. However, most existing efforts remain limited to single-modality inputs and channel-specific objec- tives, overlooking the broader potential of large foundation models for unified wireless sensing. To bridge this gap, we propose MMSense, a multi-modal, multi-task foundation model that jointly addresses channel-centric, environment-aware, and human-centered sensing. Our framework integrates image, radar, LiDAR, and textual data by transforming them into vision- compatible representations, enabling effective cross-modal align- ment within a unified feature space. A modality gating mecha- nism adaptively fuses these representations, while a vision-based large language model backbone enables unified feature align- ment and instruction-driven task adaptation. Furthermore, task- specific sequential attention and uncertainty-based loss weighting mechanisms enhance cross-task generalization. Experiments on real wireless scenario datasets show that our approach outper- forms both task-specific and large-model baselines, confirming its strong generalization across heterogeneous sensing tasks.

Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.

In this paper, deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdroppers from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the information leaked to eavesdroppers while meeting the model training energy consumption and delay constraints. To solve this problem, we propose a soft actor-critic deep reinforcement learning framework with intrinsic curiosity module and cross-attention (ICM-CA) that enables a centralized agent to determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device without knowing the position and monitoring probability of eavesdroppers. The proposed method uses an ICM module to encourage the server to explore novel actions and states and a CA module to determine the importance of each historical state-action pair thus improving training efficiency. Simulation results demonstrate that the proposed method improves the convergence rate by up to 3× and reduces the information leaked to eavesdroppers by up to 13% compared to the traditional SAC algorithm.

WiFi-based indoor positioning plays a crucial role in a variety of location-based services due to its widespread avail ability and cost-effectiveness. However, most existing indoor positioning systems predominantly utilize channel state information (CSI) to learn channel characteristics and apply fingerprinting for position estimation. Unfortunately, CSI can only be extracted from a limited set of commercial WiFi devices, hindering its widespread application in practice. In this work, we introduce BFMLoc, a novel indoor positioning framework that exploits the beamforming feedback matrix (BFM), which is readily available on commercial WiFi devices. Although BFM provides broader sensing coverage, it sacrifices detailed channel information due to the data compression applied to reduce feedback overhead. To address this limitation, we explore the feasibility of using BFM derivatives for indoor positioning and propose a U-net model to reconstruct the angle-delay profiles (ADP) from the compressed BFM data, thereby enhancing positioning accuracy. A Vision Transformer (ViT) model is then developed to extract spatial features from the predicted ADP maps to perform localization. Additionally, we design a model adaptation module based on transfer learning, integrated into the overall framework. This allows the positioning model to be easily deployed and adapted to various indoor environments with minimal retraining overhead. Extensive evaluations and validation on a digital twin testbed demonstrate that our framework achieves high positioning ac curacy and enhanced robustness compared to state-of-the-art methods.

3D reconstruction is a cornerstone for creating immersive digital experiences in metaverse. Owing to explicit scene representation and efficient rendering, Gaussian splatting (GS) has become a prominent research focus in 3D reconstruction. However, the input images for GS are often imperfect, as those collected via highly-interfered wireless environment (HIWE) tend to be distorted, thereby undermining the accuracy of 3D reconstruction and limiting scalability. This paper proposes a novel Gaussian semantic splatting (GSS) scheme, designed for agile, reliable, and communication-efficient 3D reconstruction in the metaverse. Specifically, the semantic communication encoder/decoder (SCED) within GSS performs sequential semantic encoding and channel encoding using the proposed reliable and efficient semantic communication (RESC) algorithm, enabling the receiver to recover images with near-perfect accuracy. These images are then processed by the memory-efficient Gaussian renderer (MEGR), which employs an agile Gaussian splatting rendering (AGSR) algorithm to complete the 3D reconstruction and render a series of new viewpoint images. Additionally, a semantic control unit (SCU) is designed to oversee the components, enhancing the overall efficiency of the 3D reconstruction process. Experimental results demonstrate that GSS achieves competitive 3D reconstruction quality in HIWE, delivering real-time rendering speeds of 145 FPS at an$800\times 800$resolution while reducing storage memory overhead by more than 12 times compared to the state-of-the-art (SOTA) scheme.

The Digital Twin (DT) system has become a new paradigm to empower Unmanned Aerial Vehicles (UAV) networks for low-altitude applications, such as parcel delivery. However, due to high computing complexity, traditional DT technology might confront challenges to imitating highly dynamic UAVs in large-scale parcel delivery scenarios. It causes a negative influence on low-latency and high-accuracy delivery. To address the issue, we propose a terminal-edge cooperative multi-scale DT framework. It can perform a cooperative DT implementation with a cross-layer computing resource orchestration based on a multi-scale imitation manner. Explicitly, we propose a graph matching network based DT algorithm to run macro-scale DTs at the edge. It can assist edge UAVs in exploring feasible delivery associations among UAV groups and parcel clusters based on information on UAV topology and parcel destinations for a high successful delivery ratio. We then propose a Competitive and Cooperative Reinforcement Learning (CCRL) based DT algorithm to implement micro-scale DTs at the terminal. It can enable UAVs to implement low-latency delivery by optimizing delivery paths with low energy consumption. We demonstrate the effectiveness of the proposed framework with verifications under multiple metrics. The results show that our solution provides a real-time UAV delivery performance, with up to 94% successful delivery ratio, under a low system latency compared to the state-of-the-art solutions.

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly uses two localization methods: 1) generative adversarial network (GAN) based positioning method and 2) time difference of arrival (TDOA) based positioning method. Since GAN-based method cannot defend against a strong jamming signal while TDOA-based method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to localize the target UAV. This problem is formulated as an optimization problem. The aim of this problem is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model based collaborative reinforcement learning (RL) method which enables the active UAV to optimize its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the UAVs movement and the unknown jamming attack pattern. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

3D networks with unmanned aerial vehicles (UAVs) are emerging as a cornerstone of next-generation communication infrastructure, offering flexibility and enhanced coverage in challenging environments. However, prior works predominantly focus on UAV-specific optimizations and high-throughput strategies, often overlooking the critical aspect of network reliability when incorporating these movable entities in the infrastructure. Resilience is paramount in such networks, as it ensures stable performance and connectivity in the face of dynamic conditions, such as mobile edges, transient ground devices, and significant signal interference from urban environments. To address this gap, this article proposes a topology-driven scheme from a holistic view of the 3D networks, leveraging comprehensive scene-based information to enable real-time network adaptability through topological (re)configuration. We decompose this reliability problem into three intertwined stages: topological resilience quantification, UAV self-positioning, and learning-based connectivity optimization. This framework ensures network resilience from a functional perspective, emphasizing the ability to consistently deliver high-quality performance while mitigating connectivity interruptions, essential for reliability of next-generation 3D communication infrastructure. Experimental results validate the effectiveness of our approach, demonstrating significant improvements over traditional methods in terms of bandwidth allocation to ground devices and load balancing among UAVs. Notably, our system excels in highly dynamic scenarios, where it adapts to network instability and connectivity failures on-demand, ensuring consistent and reliable communication performance.

Fingertip forces are important biomarkers for the detection and management of various conditions, including stroke and Parkinson's disease. This paper presents TouchWave, a non-contact sensing system designed to monitor fingertip forces during activities of daily living (ADL). TouchWave leverages under-cabinet millimeter-wave (mmWave) sensors to capture both macroscopic hand movements and subtle biomechanical cues associated with fingertip force production. A novel signal processing scheme is developed to suppress noise while preserving force-related information in the mmWave signals. Additionally, a hybrid deep neural network model is proposed to estimate highfidelity fingertip forces. A comprehensive evaluation involving 21 participants demonstrates the effectiveness of TouchWave in both controlled settings and ADL scenarios.

Network intrusion detection systems analyze network traffic to monitor and identify potential cyber threats. Recent research has primarily focused on enhancing detection performance using advanced deep-learning techniques, yet there is a notable gap in exploring the interpretability and transparency of these systems. Building upon advancements in large language models (LLMs) that enable reasoning-aware predictions, we propose integrating LLMs with conventional decision trees to jointly enhance interpretability, reasoning, and detection performance. Decision trees discover numerical patterns from input traffic features, which can be formulated as reasoning paths through tree traversal. These paths are then serialized into natural language descriptions and fed into LLMs to make final predictions, accompanied by detailed explanations. Such fusion strategy enables the strengths of both the numerical analysis capabilities of decision trees for pattern recognition and the embedded general logic in LLMs. Experimental results on a real-world network security dataset demonstrate multi-dimensional performance gains, even in scenarios with missing data features.

In this paper, a novel contrastive language–image pre-training (CLIP) model based on semantic The communication framework is designed. Compared to a standard neural network (e.g., convolutional neural network) based semantic encoders and decoders that require joint training over a common dataset, Our CLIP model-based method does not require any training procedures, thus enabling a transmitter to extract data meanings of the original data without neural network model training, and the receiver to train a neural network for follow-up task implementation without the communications with the transmitter. Next, we investigate the deployment of the CLIP model-based semantic framework over a noisy wireless network. Since the semantic information generated by the CLIP model is susceptible to wireless noise and the spectrum used for semantic information transmission are limited; it is necessary to optimize CLIP jointly model architecture and spectrum resource block (RB) allocation to maximize semantic communication performance while considering wireless noise, the delay and energy used for semantic communication. To achieve this goal, we use a proximal policy optimization (PPO) based reinforcement learning (RL) algorithm to learn how wireless noise affects the semantic communication performance, thus finding optimal CLIP model and RB for each user. Simulation results show that our proposed method improves the convergence rate by up to 40%, and the accumulated reward by 4x compared to soft actor-critic.

Modern network environments—spanning 5G cores, industrial IoT, and spine-leaf data-centers—offer rich hierarchical structure and multi-layer telemetry, yet deep learning models applied to these settings remain black-box predictors that ignore domain logic and struggle with scarce data. We introduce LogiK-Net, a neurosymbolic framework that bridges data and knowledge by decoupling the learning process into (i) a forward-discovery module based on Kolmogorov-Arnold Networks (KANs) that yields interpretable edge activations for feature pruning and rule mining, and (ii) a backward-validation module that employs differentiable first-order network logic to enforce domain axioms and the rules mined on-the-fly. This modular design allows practitioners to swap in richer feature extractors or stricter logical rule sets in the machine learning model as needed, scaling smoothly from supervised traffic-classification to unsupervised, open-world network management. Extensive experiments on reliable feature pruning, IoT threat detection, and topology discovery demonstrate LogiK-Net's generality, interpretability, and reliability, outperforming standard neural network baselines employed in network analysis.

WiFi-based indoor positioning plays a crucial role in a variety of location-based services due to its widespread availability and cost-effectiveness. However, most existing indoor positioning systems predominantly utilize channel state information (CSI) to learn channel characteristics and apply fingerprinting for position estimation. Unfortunately, CSI can only be extracted from a limited set of commercial WiFi devices, hindering its widespread application in practice. In this work, we introduce BFMLoc, a novel indoor positioning framework that exploits the beamforming feedback matrix (BFM), which is readily available on commercial WiFi devices. Although BFM provides broader sensing coverage, it sacrifices detailed channel information due to the compression applied to reduce feedback overhead. To address this limitation, we explore the feasibility of using BFM derivatives for indoor positioning and propose a U-net model to reconstruct the angle-delay profiles (ADP) from the compressed BFM data, thereby enhancing positioning accuracy. A Vision Transformer (ViT) model is then developed to extract spatial features from the predicted ADP maps to perform localization. Extensive evaluation results demonstrate that our framework achieves high positioning accuracy and improved robustness compared to state-of-the-art methods.

In this paper, a secure and communication-efficient clustered federated learning (CFL) design is investigated. In our model, several base stations (BSs) with heterogeneous task-handling capabilities and multiple users with non-independent and identically distributed (non-IID) data jointly perform CFL training using differential privacy (DP) techniques. Since each BS can process only a subset of learning tasks and has limited wireless resource blocks to allocate to users for federated learning (FL) model parameter transmission, it is necessary to jointly optimize resource block (RB) allocation and user scheduling for CFL performance optimization. Meanwhile, our considered CFL requires devices to use their limited data and FL model information to determine their task identities, which may introduce additional communication overhead. This problem is formulated as an optimization problem whose goal is to minimize the training loss of all learning tasks while considering device clustering, RB allocation, noise, and FL model transmission delay. To solve this, we propose a novel value decomposed multi-agent reinforcement learning (VD-MARL) algorithm that enables distributed BSs to independently determine their connected users, the RBs, and DP noise of the connected users but jointly minimize the training loss of all learning tasks across all BSs. Different from the existing MARL methods that assign a large penalty for invalid actions, we propose a novel penalty assignment scheme that assigns penalty depending on the number of devices that cannot meet communication constraints (e.g., delay), which can guide the MARL scheme to quickly find valid actions thus improving the convergence speed. Simulation results show that the VD-MARL can improve the convergence rate by up to 35% and the ultimate accumulated rewards by 27% compared to independent Q-learning.

In this paper, the framework of fluid antenna system (FAS)-assisted three dimensional (3D) passive unmanned aerial vehicle (UAV) positioning is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-assisted passive UAVs, as well as a ground base station (BS) cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs. This signal is reflected via the target UAV and received by the passive UAVs. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the BS. The BS calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate an optimization problem that optimizes the trajectories of all controlled UAVs and antenna port selection of passive UAVs with the aim of minimizing the target UAV positioning error. To address this problem, an attention-based recurrent multiagent reinforcement learning (AR-MARL) scheme is proposed. In the proposed method, a recurrent neural network (RNN) acts as a local Q function of each controlled UAV to capture its historical state-action pairs, and a transformer is used to analyze the importance of these historical state-action pairs, thus improving the global$\mathbf{Q}$function approximation accuracy, thereby further improving the positioning accuracy. Simulation results show that the proposed AR-MARL scheme can reduce the average positioning error by up to 17.5 % and 58.5 % compared to the VD-MARL scheme and the proposed method without FAS.

Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled data and significant human effort. To address these challenges, we propose ALPHA, the first Active Learning Pipeline for Human-free log Analysis. ALPHA integrates semantic embedding, clustering-based representative sampling, and large language model (LLM)assisted few-shot annotation to automate the anomaly detection process. The LLM annotated labels are propagated across clusters, enabling large-scale training of an anomaly detector with minimal supervision. To enhance the annotation accuracy, we propose a two-step few-shot refinement strategy that adaptively selects informative prompts based on the LLM's observed error patterns. Extensive experiments11The source code of our proposed ALPHA framework is available at https://github.com/Xuanhao-Luo/ALPHA. on real-world log datasets demonstrate that ALPHA achieves detection accuracy comparable to fully supervised methods while mitigating human efforts in the loop. ALPHA also supports interpretable analysis through LLM-driven root cause explanations in the post-detection stage. These capabilities make ALPHA a scalable and cost-efficient solution for truly automated log-based anomaly detection.

The integration of digital twinning technologies is driving next-generation networks toward new capabilities, allowing operators to thoroughly understand network conditions, efficiently analyze valuable radio data, and innovate applications through user-friendly, immersive interfaces. Building on this foundation, network digital twins (NDTs) accurately depict the operational processes and attributes of network infrastructures, facilitating predictive management through real-time analysis and measurement. However, constructing precise NDTs poses challenges, such as integrating diverse data sources, mapping necessary attributes from physical networks, and maintaining scalability for various downstream tasks. Unlike previous works that focused on the creation and mapping of NDTs from scratch, we explore intra- and inter-operations among NDTs within a Unified Twin Transformation (UTT) framework, which uncovers a new computing paradigm for efficient transfer, merging, and splitting of NDTs to create task-oriented twins. By leveraging joint multi-modal and distributed mapping mechanisms, UTT optimizes resource utilization and reduces the cost of creating NDTs, while ensuring twin model consistency. A theoretical analysis of the distributed mapping problem is conducted to establish convergence bounds for this multi-modal gated aggregation process. Evaluations on real-world twin-assisted applications, such as trajectory reconstruction, human localization, and sensory data generation, demonstrate the feasibility and effectiveness of interoperability among NDTs for corresponding task development.

The rapid growth in low-Earth-orbit satellites enables providing Earth observation applications to public users via a shared platform. However, the limited satellite-ground communication resources present a major challenge in downloading and fully utilizing satellite-captured Earth observation data on the ground. As a new edge computing paradigm, orbital edge computing allows satellites to host deep learning models with on-board computing resources for in-orbit data analysis, reducing downlink data volume and response time. However, the limited generalizability of in-orbit models and data distribution shifts across geographical locations severely impact the accuracy of in-orbit analytics. In this work, we design a framework, AdaOrb, which dynamically schedules online model retraining for location-specific Earth observation tasks. Scheduling decisions are made with a model predictive control-based algorithm that allocates limited satellite downlink capacity among onboard tasks to download model retraining data. By developing and using a hardware-in-the-loop orbital edge computing testbed, we show that our method achieves superior overall accuracy of in-orbit analytics tasks compared to alternative methods.

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

The rapid increase in networked systems and data transmission requires advanced data compression solutions to optimize bandwidth utilization and enhance network performance. This study introduces a novel byte-level predictive model using Transformer architecture, capable of handling the redundancy and diversity of data types in network traffic as byte sequences. Unlike traditional methods that require separate compressors for different data types, this unified approach sets new benchmarks and simplifies predictive modeling across various data modalities such as video, audio, images, and text, by processing them at the byte level. This is achieved by predicting subsequent byte probability distributions, encoding them into a sparse rank sequence using lossless entropy coding, and significantly reducing both data size and entropy. Experimental results1show that our model achieves compression ratios below 50%, while offering models of various sizes tailored for different communication devices. Additionally, we successfully deploy these models on a range of edge devices and servers, demonstrating their practical applicability and effectiveness in real-world network scenarios. This approach significantly enhances data throughput and reduces bandwidth demands, making it particularly valuable in resource-constrained environments like the Internet of Things sensor networks.

In this article, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT’s accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whether to transmit physical network information and optimizing spectrum allocation. Simulation results show that our GRU-based and VDN-based algorithm improves the weighted sum of data rates and the similarity between the status of the DNT and the physical network by up to 28.96%, compared to a baseline method combining GRU with the independent Q learning (IQL).

Due to the exponential increase in wireless devices and a diversification of network services, unprecedented challenges, such as managing heterogeneous data traffic and massive access demands, have arisen in next-generation wireless networks. To address these challenges, there is a pressing need for the evolution of multiple access schemes with advanced transceivers. Millimeter-wave (mmWave) communication emerges as a promising solution by offering substantial bandwidth and accommodating massive connectivities. Nevertheless, the inherent signaling directionality and susceptibility to blockages pose significant challenges for deploying multiple transceivers with narrow antenna beams. Consequently, beam management becomes imperative for practical network implementations to identify and track the optimal transceiver beam pairs, ensuring maximum received power and maintaining high-quality access service. In this context, we propose a Contextual Combinatorial Beam Management (CCBM) framework tailored for mmWave wireless networks. By leveraging advanced online probing techniques and integrating predicted contextual information, such as dynamic link qualities in spatial-temporal domain, CCBM aims to jointly optimize transceiver pairing and beam selection while balancing the network load. This approach not only facilitates multiple access effectively but also enhances bandwidth utilization and reduces computational overheads for real-time applications. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods.

This paper delves into the design of an energy efficient collaborative federated learning (CFL) methodology using which mobile devices exchange their FL model with a subset of their neighbors without reliance on a parameter server based on the distributed graph neural network (GNN) method. Each device is unable to send its FL model to every neighboring device due to device mobility and wireless resource limitations. To reduce the energy consumption of FL model transmission, each device must choose a subset of devices with which to share its FL model. This problem is formulated as an optimization problem to meet the constraints of delay and training loss while minimizing the energy consumption for model transmission. However, the formulated problem is difficult to solve since the device mobility patterns, and the relationship between the device connection scheme and CFL performance are unknown. To address this challenge, we analytically characterize the relationship between dynamic device connections and the performance of CFL methodology. Based on the analysis, a GNN based algorithm is proposed to enable each device to select a subset of its neighbors and the transmit power in a decentralized method. Compared to standard optimization methods that must determine device connections in a centralized manner, the GNN based method enables each device to use its neighboring devices' location and connection information to individually determine a subset of devices to transmit the local model. Given the device connections, the optimal transmit power of each device can be determined by convex optimization. Simulation results show that the proposed method can reduce the energy consumption for model transmission and training loss by up to 46% and 2%, respectively

The integration of covert communication in vehicle-to-everything (V2X) network has recently shown great potential to improve efficiency and reliability of data transmission under adversarial eavesdropping scenarios. In this paper, we propose a covert and reliable communication (CRC) framework for V2X networks, where the legitimate transmitter (Alice) attempts to communicate with a mobile receiver (Bob) in the presence of the location uncertainties of the eavesdropper (Willie). Specifically, the Bob adjusts the artificial noise power and position dynamically to communicate with Alice aided by full duplex antenna. In this context, we derive two key performance indicators of covert communication, namely the detection error probability and the effect covert throughput (ECT). Subsequently, we consider the worst case of CRC in the presence of single uncertain Willie, and derive the approximate maximum ECT expression by two-stage robust optimization. Building on this foundation, for more complex CRC scenario with multi uncertain Willies exist, we propose a deep reinforcement learning-empowered adaptation (DRLA) algorithm to maximize accumulated ECT. Extensive experiments compared to benchmarks (including stochastic selection, TD3 and DDPG) demonstrate the superiority of CRC. Specially, the designated DRLA algorithm not only can achieve a higher accumulated ECT but also can converge quickly compared with the benchmark schemes.

In this paper, a passive low earth orbit (LEO) satellite localization framework is investigated. In our considered model, one active satellite and multiple passive satellites are selected to localize a target LEO satellite, where the active satellite transmits signals to the target satellite and passive satellites receive signals reflected by the target satellite. Based on the received signals, passive satellites calculate the transmission distances and send this distance information to the active satellite that will estimate the position of target satellite. Since LEO satellites are powered by the sun, the available energy that can be used for target satellite localization is limited and dynamic. Hence, the satellite selection scheme must be optimized for improving the localization accuracy under the energy consumption constraints. This problem is cast into an optimization setting with a goal of minimizing target satellite positioning error by jointly optimizing active/passive satellite selection and transmit power allocation. To solve this problem, a mixture Gaussian distribution-based reinforcement learning (MGD-RL) method is proposed. The proposed MGD-RL method enables each LEO satellite to determine whether to be an active or a passive satellite and optimize its transmit power under the energy constraints. Furthermore, the proposed MGD-RL method can approximate the probability distribution of value functions by using mixture Gaussian distributions, thus reducing the training complexity of the designed RL. Simulation results demonstrate that, compared to a value decomposition network method and independent RL method, the MGD-RL method can improve the positioning accuracy of the target LEO satellite by up to 26.8% and 48.9%.

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the training process is coordinated by a central server. However, the server-assisted FL framework suffers from poor scalability due to a communication bottleneck at the server, and trust dependency issues. To address challenges, decentralized federated learning (DFL) architecture has been proposed to allow clients to train models collaboratively in a serverless and peer-to-peer manner. However, due to its fully decentralized nature, DFL is highly vulnerable to poisoning attacks, where malicious clients could manipulate the system by sending carefully-crafted local models to their neighboring clients. To date, only a limited number of Byzantine-robust DFL methods have been proposed, most of which are either communication-inefficient or remain vulnerable to advanced poisoning attacks. In this paper, we propose a new algorithm called BALANCE (Byzantine-robust averaging through local similarity in decentralization) to defend against poisoning attacks in DFL. In BALANCE, each client leverages its own local model as a similarity reference to determine if the received model is malicious or benign. We establish the theoretical convergence guarantee for BALANCE under poisoning attacks in both strongly convex and non-convex settings. Furthermore, the convergence rate of BALANCE under poisoning attacks matches those of the state-of-the-art counterparts in Byzantine-free settings. Extensive experiments also demonstrate that BALANCE outperforms existing DFL methods and effectively defends against poisoning attacks.

The prediction of stock prices is a highly sought-after topic in the data mining field. In recent decades, many promising methods have been proposed and widely adopted for stock price prediction. However, these methods have inherent limitations, such as low accuracy, lack of transparency, and failure to consider the interactions among stock factors. To address these issues, we propose a UNIversal and interpretable framework for enhancing Stock Price Prediction (abbreviated to UniSPP), which is capable of modeling the interactions among stock factors. UniSPP first builds a fully connected graph, where the nodes and edges are the stock factors and interactions between them, respectively. However, it is a non-trivial task to discover a proper feature interaction subgraph from a large space, especially in discrete graph modeling. Therefore, UniSPP proposes a novel idea to mine the real factor interactions by iteratively sampling subgraphs and optimizing the sampling controller. Empirical studies show that our framework can be incorporated with many popular forecasting models and can effectively discover the suitable factor interaction, which can significantly improve the prediction results of existing models.

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline.

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accuracte synchronization between the physical network and the DNT. Simulation results demonstrate that our designed algorithm can promote the weighted sum of data rates and the similarity between the status of the DNT and the physical network by up to 10.31% compared to a baseline method integrating the GRU and the deep Q network.

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.

Millimeter-wave (mmWave) communication, a cor-nerstone in the evolution of next-generation wireless networks, offers substantial bandwidth and plays a crucial role in advancing wireless connectivity capabilities. Nevertheless, the inherent directionality and susceptibility to blockages pose significant challenges for a cost-effective beam management in densely deployed networks. This paper presents a Contextual Combina-torial Beam Management (CCBM) framework, leveraging both location-aware link qualities and beam correlation to tackle the joint access point (AP) and beam selection problem in mmWave networks, with a specific focus on mitigating coordination overhead and balancing the load across APs. Built upon a formulated multi-armed bandit problem, CCBM significantly reduces the uncertainty during online probing process by employing early stopping and attention-based selection mechanisms. Theoretical analysis establishes the asymptotically optimality of the proposed approach, complemented by extensive evaluation results showcasing the superiority of our framework over other state-of-the-art schemes in multiple dimensions.

This paper investigates the correlation between building material properties and indoor network coverage, encompassing both indoor Wi-Fi and outdoor 5G technologies to provide customized network services tailored to users' needs in diverse areas. We first analyze the impact of building material characteristics, with a special focus on wall materials, on the distribution of wireless signal propagation. Then, a ray-tracing-based method is introduced to synthetically generate high-quality training data that covers fine-grained network scenarios with a wide range of wall materials, extending beyond traditional materials. This dataset serves as the foundation for our proposed Global Embedding Isomorphism Network (GemNet), a machine learning framework that facilitates the prediction of optimal material parameters for customized in-building coverage. This innovation enables architects and builders to design novel, network-friendly materials, ensuring ubiquitous and on-demand network services. Extensive evaluations consistently demonstrate a re-markable prediction accuracy of 90.52% on material parameters, underscoring the framework's ability to optimize indoor wireless network planning through the lens of material engineering.

This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-component system that includes Simulation and Data Generation, Data Collection and Upload, User-friendly Interface, Analysis and Insight, and Advisory System. Observations from three demo modules: label manipulation, attack timing, and malicious attack availability, and two analysis components: utility and analytical behavior of local model updates highlight the risks to system integrity and offer insight into the resilience of FL systems. The demo is available at https://github.com/CathyXueqingZhang/DataPoisoningVis.

Backdoor attacks can mislead deep bug search models by exploring model-sensitive assembly code, which can change alerts to benign results and cause buggy binaries to enter production environments. But assembly instructions have strict constraints and dependencies, and these additional model-sensitive assembly codes destroy semantics and syntax and are easily detected by dynamic analysis or context-based detection. To escape from the dynamic analysis-based detection, we propose a novel latent backdoor attack (LateBA) scheme based on the locality principle of program execution, which only poisons a few of infrequent execution codes, minimizing the effects on the original code logic. In LateBA, a progressive seed mutating strategy is designated to change the American Fuzzy Lop (AFL)-based path search tool to pay more attention to infrequent execution codes. With this strategy, the optimal range to positions in the whole program is determined. Subsequently, triggers are target model-sensitive assembly instructions, and try to minimize the variables that have been called in the context instructions in the trigger. Finally, we employ code semantic feature comparisons to select precise trigger injection positions within these ranges. The selection criteria of the trigger injection position is whether the corresponding code segments in this position have a data dependency relationship with other code segments. We evaluate the performance of LateBA over 7 deep bug search tasks. The results demonstrate the attack success rate of the proposed LateBA is considerable and competitive against the baselines.

The complexity of modern network infrastructure continues to grow, supporting a wide range of interconnected applications. Simulators are indispensable tools in this context, providing cost-effective and risk-free environments for experimentation and development. However, mastering these network simulators demands substantial domain-specific knowledge, even with comprehensive user manuals. Motivated by the capabilities of Large Language Models (LLMs), this paper introduces the network-oriented LLM as an intermediary between users and network simulators, aiming to offer an interactive, automated, and script-free simulation paradigm. Using the emerging Sionna simulator as a case study, we adapt the general-purpose LLM into a network-oriented LLM through joint parameter-efficient fine-tuning and retrieval-augmented generation, which then streamlines the complex simulation process through simple natural language queries. Comprehensive experiments with state-of-the-art LLMs demonstrate that the proposed method can effectively adapt LLMs for use with network simulators, significantly enhancing user-level operational efficiency and accessibility. The proposed pipeline can facilitate the broader development of various network-oriented LLMs, potentially automating a range of complex network tasks.

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, existing methods primarily focus on global scene reconstruction using large datasets, which necessitate substantial computational resources and impose high-quality requirements on input images. Nevertheless, in practical applications, users prioritize the 3D reconstruction results of on-demand specific object (OSO) based on their individual demands . Furthermore, the collected images transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of NeRF reconstruction, thereby limiting its scalability. In this paper, we propose a novel on-demand Semantic Neural Radiance Fields (OSNeRF) scheme, which offers fast and robust 3D object reconstruction for diverse tasks. Within OSNeRF, semantic encoder is employed to extract core semantic features of OSOs from the collected scene images, semantic decoder is utilized to facilitate robust image recovery under HIWE conditions, lightweight renderer is employed for fast and efficient object reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed OSNeRF enables fast and robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality.

Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is Catastrophic Forgetting, i.e., the loss of previously learned knowledge. Among existing baselines, replay-based methods show competitive results but requires extra memory for storing exemplars, while exemplar-free (i.e., data need not be stored for replay in production) methods are resource friendly but often lack accuracy. In this paper, we propose an exemplar-free approach—Forward-only Online Analytic Learning (F-OAL). Unlike traditional methods, F-OAL does not rely on back-propagation and is forward-only, significantly reducing memory usage and computational time. Cooperating with a pre-trained frozen encoder with Feature Fusion, F-OAL only needs to update a linear classifier by recursive least square. This approach simultaneously achieves high accuracy and low resource consumption. Extensive experiments on bench mark datasets demonstrate F-OAL’s robust performance in OCIL scenarios. Code is available at: https://github.com/liuyuchen-cz/F-OAL

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, the input images of NeRF transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of 3D reconstruction, thereby limiting its scalability. Fortunately, semantic communication has been proved a effective method to solve the above problem. In this paper, we propose a novel NeRF based 3D semantic communication (NeRF-3DSC) system, which offers robust 3D reconstruction in HIWE. Within NeRF-3DSC, semantic encoder and decoder are employed to extract and recover core semantic features of task-specific specific object (TSO) from the collected images, channel encoder and decoder ensure robust transmission of compressed semantic information in HIWE, lightweight renderer based on NeRF is employed for fast and efficient 3D reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed NeRF-3DSC enables robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality.

Semantic communication has emerged as a revolutionary paradigm within wireless edge networks, showcasing remarkable communication efficiency. In contrast to traditional bit-level communication systems, semantic communication systems exhibit superior effectiveness and precision, particularly in scenarios characterized by low signal-to-noise ratios (SNR). Nonetheless, the privacy of semantic communication poses a critical challenge that demands attention. Once the attacker intercepts the semantic information through continuous eaves-dropping, the private data would be leaked under adversarial environment. Moreover, in low SNR scenario, joint optimization of anti -eavesdropping and privacy reconstruction has not yet been studied, coupled with the intricate nature of designing a cross-layer semantic protection strategy. To address this concern, this paper presents a covert and reliable semantic communication (CRSC) framework via full-duplex receiver to counter continuous eavesdropper by concealing the entire transmission process. Furthermore, a newly-defined metric, namely covert semantic throughput (CST), is introduced to quantify the system's performance. Furthermore, we formulate the maximization of average CST during the semantic transmission period as a multi-constraint optimization problem. Subsequently, we propose a reinforcement learning (RL)-empowered adaptation algorithm to address the formulated problem. Through simulation results, the effectiveness and feasibility of proposed CRSC framework are demonstrated, with an observed maximum average CST improvement of up to 42% compared to conventional communication systems in the low SNR scenario.

This article introduces an approach to harness digital twin (DT) technology in the realm of integrated sensing and communications (ISACs) in sixth-generation (6G) Internet of Everything (IoE) applications. We consider moving targets in a vehicular network and use DT to track and predict the motion of the vehicles. After predicting the location of the vehicle at the next time slot, the DT designs the assignment and beamforming for each vehicle. The real-time sensing information is then utilized to update and refine the DT, enabling further processing and decision making. In the DT, an extended Kalman filter (EKF) is used for the precise motion prediction. This model incorporates a dynamic Kalman gain, which is updated at each time slot based on the received echo signals. The state representation encompasses both the vehicle motion information and the error matrix, with the posterior Cramér-Rao bound (PCRB) employed to assess sensing accuracy. We consider a network with two roadside units (RSUs), and the vehicles need to be allocated to one of them. To optimize the overall transmission rate while maintaining acceptable sensing accuracy, an optimization problem is formulated. Since, it is generally hard to solve the original problem, the Lagrange multipliers and fractional programming are employed to simplify this optimization problem. To solve the simplified problem, this article introduces both the greedy and heuristic algorithms by optimizing both the vehicle assignments and predictive beamforming. The optimized results are then transferred back to the real space for ISAC applications. Recognizing the computational complexity of the greedy and heuristic algorithms, a bidirectional long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed for efficient beamforming design within the DT. Simulation results demonstrate the effectiveness of the DT-based ISAC network. Notably, the LSTM-based RNN method achieves similar transmission rates as the heuristic algorithm but with significantly reduced computational complexity.

To reduce attack risks in Internet of Things (IoT), many security vendors conduct software security analysis on IoT devices all the time. However, how to build an ultralow-latency threat alerting strategy using software vulnerability information still faces challenges. First, existing terminal threat detection methods for IoT systems relying on Indicators of Compromise (IoC) threat intelligence can only cover limited software vulnerabilities so the alert validity rate is still very low. Second, most users lack security knowledge and cannot proactively distinguish high-risk vulnerabilities, resulting in untimely reporting. In this article, a novel hierarchical software execution state representation (HSESR) scheme is proposed for ultralow latency threat alerting over IoT systems based on Beyond 5G. In HSESR, function call graphs are recorded and delivered to edge servers for swiftly identifying suspicious threat behaviors based on deep graph representation, while corresponding instruction sequences are delivered to the cloud data center for further matching the vulnerability information via recurrent semantic representation. To improve the effectiveness of HSESR, the graph representation is also actively encapsulated into the corresponding semantic representation, together acting as an implicit threat behavior signature, which is essential to associate with a security patch. Moreover, to accelerate the detection of suspicious behaviors, we also propose a deep reinforcement learning-based graph searching (DRL-GS) strategy to crop the huge function call graph of the entire software to timely report high-risk threat behaviors with minimized resource consumption. By instancing 1-day attacks on a simulated beyond 5G IoT system, the performance of HSESR is trustfully competitive against existing baselines, and the efficiency of threat detection was increased by 21.63%.

In this article, the use of channel state information (CSI) for indoor positioning is studied. In the considered model, a server equipped with several antennas sends pilot signals to users, while each user uses the received pilot signals to estimate channel states for user positioning. To this end, we formulate the positioning problem as an optimization problem aiming to minimize the gap between the estimated positions and the ground truth positions of users. To solve this problem, we design a complex-valued neural network (CVNN) model based federated learning (FL) algorithm. Compared to standard real-valued centralized machine learning (ML) methods, our proposed algorithm has two main advantages. First, our proposed algorithm can directly process complex-valued CSI data without data transformation. Second, our proposed algorithm is a distributed ML method that does not require users to send their CSI data to the server. Since the output of our proposed algorithm is complex-valued which consists of the real and imaginary parts, we study the use of the CVNN to implement two learning tasks. First, the proposed algorithm directly outputs the estimated positions of a user. Here, the real and imaginary parts of an output neuron represent the 2D coordinates of the user. Second, the proposed method can output two CSI features (i.e., line-of-sight/non-line-of-sight transmission link classification and time of arrival (TOA) prediction) which can be used in traditional positioning algorithms. Simulation results demonstrate that our designed CVNN based FL can reduce the mean positioning error between the estimated position and the actual position by up to 36%, compared to a RVNN based FL which requires to transform CSI data into real-valued data.

In the era of 5G and beyond, the increasing complexity of wireless networks necessitates innovative frameworks for efficient management and deployment. Digital twins (DTs), embodying real-time monitoring, predictive configurations, and enhanced decision-making capabilities, stand out as a promising solution in this context. Within a time-series data-driven framework that effectively maps wireless networks into digital counterparts, encapsulated by integrated vertical and horizontal twinning phases, this study investigates the security challenges in distributed network DT (NDT) systems, which potentially undermine the reliability of subsequent network applications, such as wireless traffic forecasting. Specifically, we consider a minimal-knowledge scenario for all attackers, in that they do not have access to network data and other specialized knowledge, yet can interact with previous iterations of server-level models. In this context, we spotlight a novel fake traffic injection attack designed to compromise a distributed NDT system for wireless traffic prediction. In response, we then propose a defense mechanism, termed global-local inconsistency detection (GLID), to counteract various model poisoning threats. GLID strategically removes abnormal model parameters that deviate beyond a particular percentile range, thereby fortifying the security of network twinning process. Through extensive experiments on real-world wireless traffic data sets, our experimental evaluations show that both our attack and defense strategies significantly outperform existing baselines, highlighting the importance of security measures in the design and implementation of DTs for 5G and beyond network systems.

Optimizing edge caching is crucial for the advancement of next-generation (nextG) wireless networks, ensuring high-speed and low-latency services for mobile users. Existing data-driven optimization approaches often lack awareness of the distribution of random data variables and focus solely on optimizing cache hit rates, neglecting potential reliability concerns, such as base station overload and unbalanced cache issues. This oversight can result in system crashes and degraded user experience. To bridge this gap, we introduce a novel digital twin-assisted optimization framework, called D-REC, which integrates reinforcement learning (RL) with diverse intervention modules to ensure reliable caching in nextG wireless networks. We first develop a joint vertical and horizontal twinning approach to efficiently create network digital twins, which are then employed by D-REC as RL optimizers and safeguards, providing ample datasets for training and predictive evaluation of our cache replacement policy. By incorporating reliability modules into a constrained Markov decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints, minimizing the risk of network failures. Theoretical analysis demonstrates comparable convergence rates between D-REC and vanilla data-driven methods without compromising caching performance. Extensive experiments validate that D-REC outperforms conventional approaches in cache hit rate and load balancing while effectively enforcing predetermined reliability intervention modules.

The susceptibility of millimeter-wave (mmWave) links to blockages poses challenges for maintaining consistent high-rate performance. By predicting link quality in advance at specific locations or times of interest, proactive resource allocation techniques, such as link-quality-aware scheduling, can be employed to optimize the utilization of network resources. In this paper, we introduce a map-driven link quality prediction framework that divides the problem into long-term and short-term link quality predictions to cater to the needs of mobile computing. The first stage aims to predict a long-term radio map considering static network characteristics. We propose to separate LoS and NLoS scenarios, and build an analytical model and a regression-based approach to construct a complete link quality map in the spatial domain. Next, short-term link quality prediction is explored to anticipate future variations in link quality through a spatial-temporal attention-based prediction framework. The essence of this approach lies in capturing the spatial correlation and temporal dependency of mmWave wireless characteristics, followed by an attention mechanism to complement the dynamic link quality prediction task. On top of that, we also design a regional training mechanism with a weighted loss function to address the classical data imbalance problem of map-driven prediction. Extensive experimental and simulation results show that our integrated framework effectively captures comprehensive spatial-temporal knowledge and achieves significantly higher accuracy than other baseline prediction methods, making it a promising solution for a wide range proactive configuration tasks in mobile mmWave networks.

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively.

Millimeter-wave (mmWave) communication is a promising technology that has become a key component of next-generation wireless networks due to its large available band-width. However, the susceptibility of mmWave link to dynamic blockages makes it challenging to maintain consistently high rate performance. Hence, it is imperative to have the knowledge of link quality in advance at the location of interest to proactively optimize the use of network resources. In this work, we propose a Spatial-Temporal Attention-based Prediction (STAP) framework to predict the link quality at arbitrary locations in the presence of dynamic blockages. Specifically, our STAP model is built to capture the spatial correlation and temporal dependency of mmWave wireless characteristics in an integrated module, followed by an attention mechanism to complement the link quality prediction task. On top of that, we also design a regional training approach with a weighted loss function to address the data imbalance problem of map-based prediction. Extensive evaluation results show that our framework effectively captures comprehensive spatial-temporal knowledge and achieves significantly higher accuracy than other baseline prediction methods.

In this paper, the use of channel state information (CSI) for indoor positioning is investigated. In the considered model, a base station (BS) equipped with several antennas sends pilot signals to a user that transmits the received pilot signals back to the BS. The BS will use the received CSI data to estimate the position of the user. To this end, we formulate this positioning problem as an optimization problem aiming to minimize the mean square error between the estimated position and the actual position of the user. To solve this problem, we design a complex-valued neural network (CVNN) based positioning algorithm. Compared to real-valued neural networks (RVNNs) that need to convert complex-valued CSI data into real-valued data, the proposed method uses original CSI data to train the CVNN model for user positioning. Since the output of our proposed algorithm is complex-valued and it consists of the real and imaginary parts, we can use it to implement two learning tasks. Based on this property, two use cases of the proposed algorithm are proposed: 1) the algorithm directly outputs the estimated position of the user. Here, the real and imaginary parts of an output neuron represent the 2D coordinates of the user, 2) the algorithm outputs two CSI features (i.e., line-of-sight/non-line-of-sight transmission link classification and time of arrival (TOA) prediction) which can be used in traditional positioning algorithms. Simulation results demonstrate that our designed CVNN based algorithm can reduce the mean positioning error between the estimated position and the actual position by up to 11.1%, compared to a RVNN based method which has to transform CSI data into real-valued data.

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

To enable ultra-high throughputs while addressing the potential blockage problem, maintaining an adaptive access point (AP) planning is critical to mmWave networking. By investigating the hidden interaction between the environment map and the placement of mmWave APs, we develop an adaptive AP planning (E-app) approach that can accurately sense the environment dynamics, reconstruct the obstacle map, and then predict the placements of mmWave APs adaptively. Specifically, our solution leverages mmWave radio itself to sniff the unacceptable performance degradation through sensing only a small fraction of observation points that are identified by a sparsity-aware analytical model, thereby accurately triggering a prediction module for AP positioning when necessary. Extensive evaluations show a very high prediction accuracy for our solution, which can provide around 25% improvement on user throughput performance in mmWave WLANs. This intelligent AP-planning framework well handles the environment dynamics that affect the average-case network performance, which is of utmost interest for network deployers because of its usage convenience and adaptivity.

In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.

Although the millimeter wave (mmWave) band has great potential to address ever-increasing demands for wireless bandwidth, its intrinsically unique propagation characteristics call for different scheduling strategies in order to minimize performance drops caused by blockages. A promising approach to mitigate the blockage problem is proactive scheduling, which uses blockage predictions to schedule users when they are experiencing good channel conditions. In this paper, we formulate an optimal scheduling problem with fairness constraints that allows us to find a schedule with maximum aggregate rate that achieves approximately the same fairness as the classic proportional fair scheduler. The results show that, for the problem settings studied, up to around 30% increase in aggregate rate compared to classic proportional fair scheduling (PFS) is possible with no decrease in fairness when blockages can be accurately predicted 0.5 seconds in advance. Furthermore, aggregate rate could be doubled compared to PFS if blockages can be accurately predicted 5 seconds in advance. While these results demonstrate the very promising potential of proactive scheduling, we also discuss several future research directions that must be pursued to effectively realize the approach.

In this paper, we investigate the optimal line-of-sight (LoS) coverage problem for multiple access point (multi-AP) mmWave wireless LANs in indoor scenarios. Due to the weak diffraction ability of mmWave signals at 60 GHz, maintaining LoS communications between APs and client devices is critical to achieve ultra-high data rates with mmWave communications. We focus on the use of multiple APs deployed to maximize LoS coverage in a target area, and we develop multi-AP placements that maximize LoS coverage by means of both analytical and algorithmic methods. We consider two main scenarios, which differ in their assumptions about knowledge of obstacles and clients. In a random-obstacle, random-client scenario, we derive the LoS-optimal positions of APs by solving a thinnest covering problem. For a fixed-obstacle, random-client scenario, we propose an efficient algorithm that produces a multi-AP placement, which is shown through simulation to provide near-optimal LoS coverage. Finally, through extensive ns-3 simulations based on the IEEE 802.11ad protocol and mmWave-specific channel models, we show that our multi-AP placements are significantly better than existing placement approaches, both in terms of LoS coverage and aggregate throughput.

To accommodate increasingly intensive application bandwidth demands, mmWave WLAN at 60 GHz has been identified as a promising technology with the potential to achieve Gbps throughput. However, mmWave performance is highly dependent on the signal's line-of-sight (LoS) condition due to its high penetration loss when obstructed. We study the use of dedicated flat passive reflectors to improve coverage in indoor mmWave WLANs through a reflector placement scheme that accommodates any general indoor scenario with pre-deployed ceiling-mounted access points (APs). The reflector locations are efficiently selected among all available vertical surfaces within the indoor environment. Through simulations, we show that deployment of intelligently placed reflectors can improve LoS coverage by up to 10%, which is more than deploying one additional AP. Results are provided to illustrate how different factors affect coverage and insights about preferred reflector placements are provided.

mmWave is emerging as an essential technology for next-generation wireless networks due to its capability of delivering multi-gigabit throughput performance. To achieve such a promising performance in mmWave communications, Line-of-sight (LOS) connectivity is a critical requirement. In this work, we explore the strategy of infrastructure mobility to alter the location of an access point (AP) in order to provide LOS connectivity to stations (STAs) in indoor mmWave WiFi networks. Through both simulation-based and theoretical analyses, we make a detailed case for infrastructure mobility by identifying the impact of AP mobile platforms configurations on network performance and propose a ceiling-mounted mobile (CMM) AP model. Then, we compare the performance of a CMM AP with multiple static APs, and we identify that the throughput and fairness performance of a CMM AP is better than as many as 5 ceiling-mounted static APs.

Millimeter-wave communication is a highly promising technology to deliver multi-gigabit-per-second transmission rates for next-generation wireless LANs (WLANs). To achieve such ultra-high throughput performance in indoor scenarios, line-of-sight (LoS) connectivity becomes a critical requirement. Prior work has proposed access point (AP) mobility as an approach to improve LoS conditions and, thereby, approach optimum mmWave WLAN performance. In this work, we present a comprehensive simulation study of linear AP mobility that investigates various dimensions, including the number of mobile APs, the placement of the mobile AP platforms, and the length of the platforms. The results show how WLAN performance varies across these dimensions and also compares the results against a varying number of static APs to quantity the performance gains achievable from mobility. The results show that even 2 or 3 mobile APs can significantly outperform a much larger number of static APs and that deploying up to 3 mobile APs in a room brings substantial performance gains.

Millimeter-wave wireless LANs are targeted for use with bandwidth-intensive applications such as virtual/augmented reality and real-time high-definition video. To maintain high throughput while addressing mmWave signal blockages, multiple access points (APs) within one room to improve line-of-sight conditions is considered a promising approach. In a scenario with fixed and mobile (human) obstacles, we mathematically analyze LoS blockages produced by mobility, and use the analysis to develop a multi-AP association scheme. Our scheme statically assigns primary and backup APs in order to maximize blockage robustness and perform load balancing among APs. Simulation results show that: 1) our static approach can provide blockage tolerance close to that of an expensive dynamic probing approach while achieving higher throughput, 2) the use of client mobility patterns, if known, can improve our static approach even further, and 3) our approach achieves significantly better fairness and load balancing than existing approaches.

mmWave communication is a highly promising technology for 5G wireless backhaul. However, network performance is hard to predict due to the sensitivity of mmWave signals to blockages. In this paper, we propose an analytical framework to incorporate blockage effects and evaluate blockage robustness within a previously proposed interference-free topology for roadside relay-assisted mmWave backhaul. Through stochastic geometric analysis, the blockage probabilities for four types of blockages identified in prior work are derived as a function of the topology parameters and obstacle density. Analysis of the effect of topology parameters on blockage probability yields insight that leads to a modified topology, which maintains the desirable interference-free property but has better blockage robustness than the original topology. Simulation results demonstrate that the modified topology can maintain very high throughput and has significantly improved robustness as compared to the original topology, while using the same number of relays.

Line-of-sight (LOS) is a critical requirement for mmWave communication. In this work, we make the case for a ceilingmounted mobile (CMM) AP by comparing its performance with other types of AP mobility and single static AP. We then present Hawkeye to solve the optimal location discovery problem for a CMM AP using a machine learning (ML) algorithm. Hawkeye relies purely on the connectivity matrix between STAs and the AP to decide if and where the AP should move to for maximizing LOS connectivity. Using a prototype implementation, we show that the throughput of Hawkeye is 219% and 129% compared with single static AP and other approaches for AP mobility, respectively.

mmWave communication has been recognized as a highly promising technology for 5G wireless backhaul, which is capable of providing multi-gigabit per second transmission rates. However, in urban wireless backhaul environments, unforeseen events can cause short-term blockages or node failures and, therefore, network survivability is extremely important. In this paper, we investigate a novel relay-assisted mmWave backhaul network architecture, where a number of small-cell BSs and relays are deployed, e.g. on the lampposts of urban streets. Relays are used to provide multi-hop line-of-sight paths between small-cell BSs, which form logical links of the network. In this scenario, the interconnected logical links make up a mesh network, which offers opportunities for both link-level and network-level reconfiguration. We propose two joint link-network level reconfiguration schemes for recovery after exceptional events. One prioritizes relay path (link-level) reconfiguration and uses alternate network-level paths only if necessary. The other splits traffic on both reconfigured links and backup paths to improve network throughput. Simulation results demonstrate that the proposed schemes significantly outperform purely link-level and purely network-level reconfiguration schemes. The proposed approaches are shown to not only maintain high network throughput but to also provide robust blockage/fault tolerance across a range of scenarios for urban mmWave backhaul networks.

mmWave communication in 60GHz band has been recognized as an emerging technology to support various bandwidth-hungry applications in indoor scenarios. To maintain ultra-high throughputs while addressing potential blockage problems for mmWave signals, maintaining line-of-sight (LoS) communications between client devices and access points (APs) is critical. To maximize LoS communications, one approach is to deploy multiple APs in the same room. In this paper, we investigate the optimal placement of multiple APs using both analytical methods and simulations. Considering the uncertainty of obstacles and clients, we focus on two typical indoor settings: random-obstacle-random-client (RORC) scenarios and fixed-obstacle-random-client (FORC) scenarios. In the first case, we analytically derive the optimal positions of APs by solving a thinnest covering problem. This analytical result is used to show that deploying up to 5 APs in a specific room brings substantial performance gains. For the FORC scenario, we propose the shadowing-elimination search (SES) algorithm based on an analytic model to efficiently determine the placement of APs. We show, through simulations, that with only a few APs, the network can achieve blockage-free operation in the presence of multiple obstacles and also demonstrate that the algorithm produces near-optimal deployments. Finally, we perform ns-3 simulations based on the IEEE 802.11ad protocol at mmWave frequency to validate our analytical results. The ns-3 results show that proposed multi-AP deployments produce significantly higher aggregate performance as compared to other common AP placements in indoor scenarios.

With the increasing use of bandwidth-hungry applications on mobile devices, mmWave communication is considered a key enabling technology for 5G cellular networks. One very promising use of mmWave communication is in wireless backhaul for 5G. In this paper, we consider wireless backhaul links deployed along the side of a road, which will be a common scenario both in urban environments and on highways. We investigate blockage robustness within an interference-free topology previously proposed for roadside wireless backhaul. Reconfiguration algorithms are provided both for the case where rescheduling is possible after reconfiguration and for the case that the original transmission schedule must be maintained. We prove that our reconfiguration algorithms are guaranteed to maintain connectivity under several obstacle scenarios. We also evaluate the algorithms' performance with varying numbers of randomly-placed obstacles through simulation. Results show that our algorithms not only achieve high throughputs close to the no-blockage case, but also provide high blockage tolerance rates for the common case of a few obstacles along a several hundred meter section of a road.

The space information network (SIN) has attracted more and more attention due to its extensive applicability and great expanding access services. The complicated properties of SIN, such as the dynamic and unstable topology, the highly exposed links and so on, make it necessary to design an appropriate key management scheme to ensure the security of communication. In this paper we propose an anonymous and distributed certificate-less key management scheme (aCL-KMS) for SIN. It mainly adopts the strategy of the distributed key generation, update and agreement instead of the complex centralized key management. Based on the certificate-less public key cryptosystem (CL-PKC), this scheme not only avoids the high cost of complicated certificate management, but also overcome the key-escrow problem of the certificate-based or identity-based public key cryptosystem. Also, due to the fact that the anonymous authentication mechanism adopts the temporary identification of members, this scheme can efficiently protect the members' privacy and ensure the confidentiality of communications. The security properties discussion and the computational overhead analysis show that the proposed key management system is secure enough to meet the security requirements of SIN, and it is of less computing cost at the same time.