Effective recognition of jamming in a communication system is essential to maintain the integrity of the electromagnetic spectrum space. In this paper, a novel feature-enhanced open-set jamming pattern recognition method (FOSR) is proposed. First, an in-phase and quadrature (I/Q) data feature enhancement module is designed based on a complex-valued autoencoder to capture the interaction features between the I and Q channels. Then, a jamming feature extraction module is designed to extract jamming characteristics for known patterns by integrating the raw I/Q data with their interaction features. Subsequently, an adaptive threshold open-set classification module is proposed to recognize both known and unknown patterns. Finally, to address the domain shift problem, we extend FOSR with a domain adaptation (DA) module based on distribution alignment and classifier calibration, referred to as FOSR-DA. Simulation results show that the proposed method achieves superior recognition accuracy and exhibits strong robustness when dealing with the domain shift problem.

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6 G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the “value of information” as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6 G networks.

Fine-tuning large language models (LLMs) is critical for adapting pretrained models to specialized downstream tasks. Federated LLM fine-tuning enables privacy-aware model updates by allowing data owners (DOs) to contribute a global LLM without exposing local data. However, full-parameter fine-tuning in federated settings incurs significant computational and communication overhead, while frequent gradient exchanges increase the risk of privacy leakage, such as memorized data inference. Parameter-efficient fine-tuning (PEFT) with differential privacy (DP) offers a low-overhead alternative with formal privacy guarantees, but fails to strike privacy-utility tradeoff under heterogeneous privacy preferences: individual DOs may inject excessive DP noise to maximize privacy, whereas the curator aims to minimize noise to preserve model quality. In this paper, we present an innovative game-theoretical framework that enables dynamic privacy trading within differentially private federated LLM fine-tuning. In the game, DOs strategically adjust their local DP noise levels in exchange for customized incentives from the curator, thereby balancing privacy and utility. We begin by establishing a theoretical convergence bound that quantifies the influence of locally injected noise on the global model utility. Under this bound, we analytically characterize the pure-strategy Nash equilibrium of the game, accounting for DO heterogeneity, curator budget constraints, and noise estimation errors. For mixed-strategy settings with incomplete information, we design a hierarchical reinforcement learning algorithm that jointly learns DOs’ optimal noise-saving strategies and the curator’s optimal pricing policy without presupposing their private information. Experiments on real-world datasets demonstrate that the proposed scheme improves DO utility, reduces curator cost, mitigates free-riding, and accelerates convergence compared to existing methods.

In this paper, we investigate fairness-aware intelligent reflecting surface (IRS)-assisted multiple-user uplink communication systems. Current IRS-assisted communication methods do not consider the fairness issues of communication performance for multiple users. We formulate the IRS phase shift and receive beamforming design as an α-fair utility maximization problem by considering different fairness metrics, including zero fairness and proportional fairness, followed by an iterative algorithm to solve this problem. To overcome the high computational complexity of the iterative algorithm, we introduce a deep learning-based framework, which leverages convolutional neural networks with residual and attention mechanisms for real-time phase shift prediction. Extensive numerical simulations demonstrate that the proposed method achieves performance comparable to the traditional approach while significantly reducing computational overhead, making it a promising solution for future fairness-aware IRS-assisted communication systems.

Due to the fixed bidding and matching process in traditional sealed-bid spectrum auctions, participants with overlarge demands may not be matched, resulting in suboptimal total social welfare and low spectrum utilization. To address this problem, we propose a flexible bidding double spectrum auction scheme, where each buyer can submit both a base bid and an additional bid based on their basic spectrum demand and additional spectrum needs. Then we propose a two-step spectrum auction mechanism: the Sort-based matChing And vickRey Pricing (SCARP) mechanism for base bids in the first step, and the Fairness-based aLlocation And Pricing (FLAP) mechanism for additional bids in the second step. Furthermore, we prove that the proposed mechanism satisfies the truthfulness, budget balance, and individual rationality properties. Simulation results demonstrate that the proposed flexible bidding scheme outperforms the benchmark scheme, significantly improving the social welfare and spectrum utilization.

Spectrum management for the coexistence of multiple unmanned aerial vehicle (UAV) networks is a challenging issue, considering both space and frequency reuse. To address this issue, we propose a joint bandwidth and spectrum usage zone (SUZ) flexible allocation scheme, leveraging interference graph. We formulate a joint spectrum bandwidth allocation and SUZs adjustment problem to maximize the system utility, which is a binary nonlinear programming (BNLP) problem. Then we decompose it into two subproblems: the high-priority UAV networks subproblem and the low-priority UAV networks subproblem. The high-priority subproblem is solved using a graph coloring method based on the interference graph, whereas the low-priority subproblem is addressed through a sequential one-step block coordinate descent (SOBCD) approach by constructing a spectrum assignment hypergraph. Simulation results demonstrate that the total utility with the proposed scheme outperforms benchmark schemes, and there exists an optimal SUZ grid adjustment to maximize the total utility.

As a malicious attack targeting on the GPS receiver, GPS spoofing attack interferes the normal received satellite signal by reproducing or relaying the signal, resulting in severe position deviation. Such attack has posed significant security threat to unmanned-aerial-vehicles (UAVs), especially in the era of low-altitude economics. However, due to the similarity of the spoofing and intended signal, and the presence of noise, accurate detection and recognition of GPS spoofing attack still remains a challenging issue, particularly in the case of limited samples. In this article, we apply the AdaBoost-CNN algorithm, which combines multiple weak convolutional neural network (CNN) classifiers into a strong classification model, to achieve GPS spoofing attack recognition. To further improve the recognition accuracy when there are very limited samples, we improve the AdaBoost-CNN algorithm by transferring previous network parameters to subsequent CNN. Both simulated and real measurement data are employed to verify the effectiveness of the proposed scheme. It is shown that the recognition accuracy can reach up to 93.75% and 95.83% with 160 simulated samples and 120 measured samples, respectively.

Due to the high flexibility and versatility, uncrewed aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue. However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39%, 12.74%, and 16.6% than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.

Cryptocurrencies, underpinned by blockchain technology, face persistent threats such as money laundering and extortion due to their decentralized and anonymous nature. Detecting fraudulent transactions is crucial for ensuring the security of block-chain systems. However, the existing detection methods face the following challenges: lack of labeled data, severe class imbalance in labeled data, complex network structure, numerous parameters, and long training time. To address these challenges, we propose a novel semisupervised learning framework that combines the graph augmented with pseudolabels generation (GAPLG) model and postprocessing technique. Our framework employs graph learning networks to elucidate relationships between transactions and users. By utilizing pseudolabels for unlabeled transaction data and embedding them onto diverse graph nodes, we achieve precise labels, enhancing prediction accuracy. Additionally, we employ specific post-processing technique, such as correction and smoothing (C&S) technology, to rectify residuals and refine labels, ensuring our framework rivals the best parameter and baseline models. Our method boasts high scalability and flexibility, aiding in optimizing various evaluation indicators. Experimental verification through multiple real transaction datasets under varying data segmentations, demonstrated its effectiveness when compared with other representative frameworks. The analysis validates the effectiveness and benefits of our method.

In this paper, we propose a deep learning (DL)-based task-driven spectrum prediction framework, named DeepSPred. The DeepSPred comprises a feature encoder and a task predictor, where the encoder extracts spectrum usage pattern features, and the predictor configures different networks according to the task requirements to predict future spectrum. Based on the DeepSPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i.e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB. 3D-SwinSTB unique3D Patch Merging ViT-to-3D ViT Patch Expandingand pyramid designs help the model accurately learn the potential correlation of the evolution of the spectrogram over time. Then, we propose a novel spectrum occupancy rate (SOR) method by redesigning a predictor consisting exclusively of 3D convolutional and linear layers to serve possible applications such as dynamic spectrum access (DSA) task, named 3D-SwinLinear. Unlike the 3D-SwinSTB output spectrogram, 3D-SwinLinear projects the spectrogram directly as the SOR. Finally, we employ transfer learning (TL) to ensure the applicability of our two methods to diverse spectrum services. The results show that our 3D-SwinSTB outperforms recent benchmarks by more than 5%, while our 3D-SwinLinear achieves a 90% accuracy, with a performance improvement exceeding 10%.

With the development of low-altitude network, cross-domain collaboration between unmanned aerial vehicle (UAV) and ground station is becoming increasingly important. To ensure the legitimacy of identity, it is imperative to design a cross-domain authentication scheme. However, current cross-domain authentication schemes still face challenges such as low computational efficiency and high storage overhead, and still have some security vulnerabilities. This paper proposes a cross-domain authentication and key agreement scheme for UAVs, which primarily employs a combination of symmetric encryption and hash functions. By holding cross-domain tokens, UAVs can directly engage in cross-domain authentication, ultimately achieving key agreement with ground station. We conduct security and performance analyses, demonstrating the feasibility of our scheme in resource-constrained low-altitude network.

In the evolving landscape of sequential recommendation systems, this paper propels the frontier forward with the introduction of the knowledge graph enhanced multi-task learning (KGML) model. At its core, KGML harnesses the capability of big data analytics, enabling a nuanced understanding of both the immediate and enduring interests of users. This is achieved through the integration of item knowledge graphs and multitask learning, which are meticulously enriched with big data insights, thereby ensuring a comprehensive representation of item attributes and interconnections. Such a method not only elevates the model’s precision in tailoring recommendations for less popular items with limited data but also effectively counters the "Matthew Effect", where visibility becomes disproportionately skewed towards already popular items. Through rigorous validation across three public datasets, the KGML model demonstrates that the proposed approach significantly enhances the accuracy of sequential recommendations.

With the advent of the Industry 4.0 era, the widespread deployment of Automated Guided Vehicles (AGVs) in factories has enabled them to serve as sensor relays, assisting in collecting sensor data in areas with poor signal quality. Traditionally, the objective of sensor data collection has been primarily to reduce the delay in data acquisition. However, latency alone offers an incomplete reflection of the significance of sensor data to industrial tasks. Value of information (VoI) has emerged as a novel metric that more accurately reflects the impact of sensor data on the performance of upstream tasks. In this background, we introduce an innovative AGV-assisted sensor data collection strategy to minimize the loss of sensor data VoI. This strategy encompasses the selection of data fusion nodes, choice of transmission modes, and AGV path planning. We introduce a new metric called structural value entropy, which effectively reduces VoI loss during the data fusion process, and through the design of a metaheuristic algorithm based on ant colony optimization, achieves the selection of transmission modes and the planning of AGV paths with minimal VoI loss. Simulation experiments validate the effectiveness of the proposed strategy in maintaining VoI, demonstrating significant performance enhancements and acceptable convergence speed compared to baseline strategies, affirming the strategy's efficiency and feasibility in handling large-scale sensor data collection tasks.

Modern internet applications exhibit characteristics of distribution and diversity. Cross-domain authentication becomes necessary when applications or services are located in different domains. The security and efficiency of information interaction is closely related to the security and efficiency of cross-domain authentication. Existing cross-domain authentication models mostly rely on trusted third parties, which pose heavy key management and private key escrow problems. A secure and efficient cross-domain authentication scheme based on blockchain is proposed in this text. The scheme uses hash function and digital signature to ensure the reliability of foreign user identity. The proposal introduces blockchain and some ideas of the Open Shortest Path First (OSPF) dynamic routing protocol.

Federated learning (FL) that improves data privacy reduces the computational overhead for Internet of Vehicles (IoV) systems but has difficulty in defending against selfish attacks due to the restricted quality of service requirements and the high mobility of vehicles. In this paper, we design a risk-aware hierarchical reinforcement learning-based FL framework for IoV to resist selfish attacks. By designing a two-level hierarchical policy selection module that consists of two deep neural networks, this framework divides the training policy into two sub-policies, i.e., the selection of FL participants and the corresponding local training data size, which are chosen based on the previous training performance and vehicle participation performance. This framework designs a risk-aware safety guide to avoid dangerous states such as local task failure resulting from risky training policies. Specifically, the guide uses a warning signal to evaluate the short-term risk of each state-action pair, applies an R-network to estimate the long-term risks for modifying the chosen training policy, and designs a punishment function for the modified training policy to revise the immediate reward to further enhance the safe exploration. We analyze the convergence performance and computational complexity of our scheme. Experimental results on MNIST, CIFAR-10, and Stanford Cars datasets verify the effectiveness of our scheme, including the global model accuracy, training latency, detection success rate, and convergence speed compared with the benchmarks FedAvg, MFL, DQNPS, and SHRL.

With the rapid growth in the number of high-mobility vehicles and booming enhanced applications with restricted latency requirements, downlink communication in Internet of Vehicles (IoV) systems has become increasingly vulnerable to active eavesdropping attacks. This paper proposes a federated learning-enabled secure communication framework for IoV against active eavesdropping, in which the roadside units (RSUs) apply reinforcement learning (RL) model to optimize their downlink transmit power levels, and the server helps update the RL models of the RSUs. First, we design a multi-agent deep RL algorithm for each RSU, which designs a punishment and a blacklist mechanism to mitigate risky explorations related to severe data leakage or communication outages. Second, this framework designs a risk-aware RL for the server, which uses a two-level hierarchical structure to choose the number of participated RSUs and the corresponding local training data size for higher optimization speed. This framework considers both the reward and risk in the selection of policies to reduce the probability of exploring the risky training policies that cause defense failure of the RSUs against active eavesdropping. Third, we analyze the convergence performance, computational complexity, and reward upper bound, which reveals how the power constraint, radio bandwidth and data size affect the secure communication performance. Simulation and experimental results validate the effectiveness of our schemes, such as the reductions of the eavesdropping rate, training latency, and the loss of local models compared to the benchmarks.

Leveraging the unique characteristics of blockchain, secure and efficient dynamic spectrum sharing (DSS) can be achieved, which has been regarded as a promising solution to meet the spectrum requirement in future wireless communication systems. However, proper incentive mechanism with guaranteed fairness is essential for blockchain-enabled DSS. In this paper, we investigate fairness-oriented spectrum auction, where multiple access points can share resources on the blockchain platform with smart contract. Specifically, we propose a fairness factor to adjust users’ satisfaction considering both the historical spectrum allocation results and current spectrum auction results. Then, an improved virtual auction mechanism is proposed to balance the long-term satisfaction of participants. Simulation results show that the multi-round fairness-based auction algorithm (FBAA) can enhance the fairness of spectrum allocation and increase the number of radio users served.

In this paper, we investigate the secure transmission of multi-resource heterogeneous radio access networks (RANs) in space-air-ground integrated network (SAGIN) from the perspective of physical layer security. Considering the network heterogeneity, resource constrain, and channel similarity, it is challenging to implement the physical layer security in SAGIN. Particularly, digital twin (DT) is considered in the cyberspace of SAGIN to reflect the physical network entities (i.e., satellite, unmanned aerial vehicle (UAV), and terrestrial base station), which is assumed to comprehensively control and manage the heterogeneous RANs’ resources. To ensure secure transmissions of multi-tier heterogeneous downlink communications in SAGIN, a multi-point symbiotic security scheme is proposed through DT-assisted multi-dimensional domain synergy precoding, where the co-channel interference due to spectrum sharing among these heterogeneous RANs is recast to unevenly corrupt the main and wiretap channels of each legitimate user. Specifically, to realize the multi-point symbiotic security, a max-min problem is formulated to maximize the minimum secrecy rate of three heterogeneous downlinks. Since this problem is non-convex and challenging, a list of mathematical reformulations is derived and the successive convex approximation (SCA) based multi-dimensional domain synergy precoding algorithm is proposed to solve it. Moreover, the computational complexity of our proposed approach is analyzed and meaningful discussions are made. In addition, extensive simulations are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

Mobile edge computing is considered as a promising solution to mobile blockchain system, where mobile nodes with limited computing capability may participate the mining process by offloading the computing intensive mining tasks to nearby edge service providers (ESPs). To mitigate the collusion between ESP and miners, we propose a reward sharing model in this paper, where each miner will share part of the mining rewards to ESP to build a mutual cooperation group. We model the interaction among the ESP and miner nodes as a two-stage Stackelberg game. Then, we obtain the optimal edge computing demand and the corresponding price of each miner by solving the Nash equilibrium of the game iteratively with gradient descent method. We also compare the revenues under both collusion and normal mode with the proposed reward sharing scheme. Simulation results show that the more mining rewards that miners share to ESP, the higher the computing resource demands of miners and the less of the unit price of the computing resources. In addition, if the block size of the colluding miner is much larger than other miners, the ESP can obtain higher revenues rather than colluding.

In this article, we develop an analytical framework to study the impact of location-based relay selection strategy on the reliability of cognitive relay networks. By utilizing the tool of stochastic geometry, we first derive a closed-form expression for the reliability-enhanced region (RER), where relaying transmission can achieve higher transmission reliability than direct transmission. Then, we adopt the normalized reliability gain (NRG) to quantify the reliability benefit obtained by using relaying transmission compared to direct transmission, and we obtain the spatial distribution of NRG in the RER. Subsequently, by taking the spatial random nature of relays’ distribution into account, we investigate the reliability benefit obtained by secondary networks with the optimal location-based relay selection (OLB-RS) strategy. To reduce the feedback overhead during relay selection, we propose a region-aware relay selection (RA-RS) strategy and obtain the achievable reliability benefit. The results indicate that the reliability is highly dependent on the location of relay, and the OLB-RS strategy is to select the relay closest to the midpoint between the corresponding secondary source and destination.

In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices significantly reduces the downlink training length (DTL) and channel feedback. Nevertheless, most of the TSB methods focus on the one-ring channel. In this paper, we consider the multiple scatterer clusters (MSC) channel in massive MIMO systems and propose a TSB method based on cluster group. To reduce the channel state information (CSI) feedback, for each cluster we use a cluster-group-based eigen-prebeamformer to sparsify the effective channel matrix. The DTL is also reduced by a graph-based downlink training design. We further develop a multi-user beamformer to mitigate the inter-user interference. To further reduce the DTL, two other methods are also proposed based on the vertex and edge deletion. Simulation results confirm the efficiency of the proposed schemes in improving the effective spectral efficiency.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted physical layer security in multi-beam satellite enabled vehicle communications. Particularly, the UAV is exploited as a relay to improve the secure satellite-to-vehicle link, and simultaneously serves as a jammer by deliberately generating artificial noise (AN) to confuse Eve. The satellite beamforming (BF) and UAV power allocation (PA) are jointly optimized to maximize the secrecy rate of the legitimate user within a target beam while guaranteeing the quality of service (QoS) of users within other beams. Since the problem is nonconvex, we first convert it into an equivalent two-stage problem. Then, the outer-stage problem is solved by using one-dimensional search, and the inner-stage problem is transformed to a bi-convex problem by using the semi-definite relaxation (SDR) and Charnes Cooper transformation. To solve the inner-stage bi-convex problem, we propose an iterative alternating optimization algorithm, where the optimal BF is obtained by semi-definite programming (SDP), and the optimal UAV PA is subsequently obtained by solving the reformulated fractional programming problem with an iterative Dinkelbach method. The tightness of SDR and the complexity of our proposed approach are analyzed, and extensive simulations are carried out to evaluate the effectiveness of our proposed approach.

With the help of non-orthogonal multiple access (NOMA), the number of connections of the beamspace multiple-input multiple-output (MIMO) systems can be improved with enhanced sum-rate performance, which constitutes beamspace MIMO-NOMA. Thus, most relevant papers focus on improving the system sum rate, which may inflict unbearable rate loss to weak users. To ensure the achievable rates of weak users, we maximize and analyze the minimal rate of the system in the single-beam case as well as the multi-beam case, where two completely different phenomena are revealed. Particularly, in the single-beam case, the maximized minimal rate of the beamspace MIMO-NOMA always grows rapidly with the signal-to-noise-ratio (SNR), and is larger than that of the beamspace MIMO using orthogonal multiple access (beamspace MIMO-OMA). However, in the multi-beam case, the maximized minimal rate of the beamspace MIMO-NOMA grows slower and slower in the high-SNR region, where it is smaller than that of the beamspace MIMO-OMA. To explain this difference, it is disclosed that the intra-beam interference in the single-beam case is ofsuccessive pattern, which is proved to have no limit on the max-min rate. In contrast, the inter-beam interference in the multi-beam case is ofmutual pattern, which is proved to restrict the max-min rate to a derived upper bound.

In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.

By fully utilizing the caching space of nearby smart users, mobile user caching is a promising solution for efficient content delivery service. However, how to motivate mobile users to share their spare caching space and protect the privacy information during caching sharing is a challenging issue. In this paper, we consider a blockchain enabled mobile user caching on the basis of blockchain network, thus the security and privacy issues can be well addressed, and the contributions of each mobile user can be recorded in the blockchain. Specifically, the interactions between the content service provider and the mobile users are modelled as a Stackelberg game, and the equilibrium is analyzed. Simulation results demonstrate the effectiveness of the proposed scheme and it is shown that the optimal strategies of content service provider and caching users can be established with different cache cost and caching requirement.

Channel estimation and signal detection are essential steps to ensure the quality of end-to-end communication in orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we develop a DDLSD approach, i.e., Data-driven Deep Learning for Signal Detection in OFDM systems. First, the OFDM system model is established. Then, the long short-term memory (LSTM) is introduced into the OFDM system model. Wireless channel data is generated through simulation, the preprocessed time series feature information is input into the LSTM to complete the offline training. Finally, the trained model is used for online recovery of transmitted signal. The difference between this scheme and existing OFDM receiver is that explicit estimated channel state information (CSI) is transformed into invisible estimated CSI, and the transmit symbol is directly restored. Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.

Considering the high-density and high-dynamic feature of cooperative unmanned aerial vehicles (UAVs) swarm, also referred to as flocking of flying ad hoc networks (FANETs), reliable medium access control (MAC) protocol design for network connectivity maintaining and network information sharing is a challenging issue. In this article, we propose a self-adaptive carrier sense multiple access with collision avoidance (CSMA/CA)-based MAC protocol for flocking of FANET, namely, FMAC, to provide reliable broadcast information service under density-varying flocking scenarios. To represent the varying trend of UAV density during flocking, we define the collective neighboring potential (CNP) in the FMAC protocol. Specifically, at the beginning of each period, each UAV computes the current CNP based on available neighbors' motion states. Then, the value of CNP at the start of the next period regarding the same neighbors is predicted using UAV's kinetic equation. After that, each UAV can update the contention window (CW) size by comparing the current CNP and the predicted CNP, and CW will be decreased (increased) if the current CNP is larger (smaller) than the predicted one for enough period. The simulation results show that the proposed FMAC protocol can ensure high successful transmission probability under density-varying flocking scenarios and outperforms the typical MAC solutions.

In this paper, we propose a secure computationoffloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security. Existing computation offloading schemes usually neglected time-varying characteristics of channels and their corresponding secrecy rates, resulting in an inappropriate task partition ratio and a large secrecy outage probability. To address these issues, we utilize an ergodic secrecy rate to determine how many tasks are offloaded to the edge, where ergodic secrecy rate represents the average secrecy rate over all realizations in a time-varying wireless channel. Adaptive wiretap code rates are proposed with a secrecy outage constraint to match time-varying wireless channels. In addition, the proposed secure beamforming and artificial noise (AN) schemes can improve the ergodic secrecy rates of uplink and downlink channels even without eavesdropper channel state information (CSI). Numerical results demonstrate that the proposed schemes have a shorter system delay than the strategies neglecting time-varying characteristics.

In this article, we address the security issue in a tiered small-cell network aiming at security optimization for small-cell users (SUEs) to defend against eavesdropping. Meanwhile, the transmissions from small-cell base stations (SBSs) are subject to the aggregate interference constraints of macro-cell users (MUEs). In particular, we consider two-fold information uncertainties in small cells, i.e., the uncertainties regarding the eavesdroppers and interference channels to the MUEs. As such, the SBSs compete for robust secrecy rate with robust protection for the MUEs. We adopt the generalized robust Nash equilibrium problem (GRNEP) formulation, for which we confirm the existence of equilibrium and analyze the condition for the uniqueness with variational inequality-assisted analysis. Furthermore, to solve for the equilibrium, we introduce the pricing mechanism and decompose the original GRNEP as a nonlinear complementarity problem with a priced NEP, where the former provides solution of price coefficients and the latter for resource allocation strategies based on given prices. Finally, extensive simulation results are provided to demonstrate the impacts of the interference constraint and uncertainties upon the security performance of an individual SUE and the overall network, which also corroborate the effectiveness of our proposal in security provisioning for the SUEs and interference protection for the MUEs.

Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network planning and management. Considering diverse users' activities at different locations and times, it is technically challenging to characterize the network resource demands at different time scales via traditional prediction methods. In this paper, we propose to combine the long short-term memory (LSTM) and Gaussian process regression (GPR) to achieve accurate single-cell level cellular traffic prediction, using the open Milan cellular traffic dataset provided by Telecom Italia. Firstly, the dominant periodic components of the cellular data are extracted, and then the small components are fed to the LSTM network. To further improve the prediction accuracy, GPR is used to recover the residual components. Extensive experiments are conducted based on the dataset, and it is shown that the proposed LSTM-GPR scheme outperforms the benchmark schemes, especially for a relatively long time and burst traffic prediction.

To enable large-scale and ubiquitous automotive network access, traditional vehicle-to-everything (V2X) technologies are evolving to the Internet of Vehicles (IoV) for increasing demands on emerging advanced vehicular applications, such as intelligent transportation systems (ITS) and autonomous vehicles. In recent years, IoV technologies have been developed and achieved significant progress. However, it is still unclear what is the evolution path and what are the challenges and opportunities brought by IoV. For the aforementioned considerations, this article provides a thorough survey on the historical process and status quo of V2X technologies, as well as demonstration of emerging technology developing directions toward IoV. We first review the early stage when the dedicated short-range communications (DSRC) was issued as an important initial beginning and compared the cellular V2X with IEEE 802.11 V2X communications in terms of both the pros and cons. In addition, considering the advent of big data and cloud-edge regime, we highlight the key technical challenges and pinpoint the opportunities toward the big data-driven IoV and cloud-based IoV, respectively. We believe our comprehensive survey on evolutionary V2X technologies toward IoV can provide beneficial insights and inspirations for both academia and the IoV industry.

In this paper, we propose covert localization to improve the security of wireless localization networks, which can prevent the legitimate transmission of localization signals between anchors and agent from being detected by the illegitimate warden. Specifically, we first establish a framework of covert localization and demonstrate its feasibility when the warden suffers noise uncertainty. Then, with two specific noise uncertainty distributions, we derive the fundamental limit of localization accuracy, i.e., covert squared position error bound (CSPEB), which is the achievable localization accuracy for the agent while ensuring covertness for the warden. Theoretical analysis of CSPEB demonstrates the impact of different factors on the localization accuracy. Besides, in an energy-constrained scenario, we formulate a power allocation problem to refine anchors' power to minimize the CSPEB for a given total power budget and develop an algorithm based on the semidefinite program (SDP). Simulation results verify our theoretical analysis by evaluating the effect of several representative factors on the CSPEB and show the superiority of the SDP-based power allocation algorithm to the other baseline methods.

In this paper, we study a multi-beam millimeter- wave beamspace multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (NOMA) to simultaneously accommodate multiple users in a single beam. To improve the data rate while maintaining user fairness, we analyze the max-min rate of the system via power allocation. The challenge is that the existence of both the intra- beam and inter-beam interference makes the power- allocation problem non-convex. To address this issue, we devise a bisection approach to calculate the max-min rate and the corresponding power allocation. We prove that the max-min rate can be achieved when all the users are assigned the same rate. Furthermore, our endeavors reveal that beamspace MIMO-NOMA outperforms the traditional beamspace MIMO in terms of the minimal rate when the power or the number of users is relatively small. When the power or the number of users is relatively large, traditional beamspace MIMO can outperform beamspace MIMO-NOMA since the former is free of inter-beam interference, which has been verified by simulation results.

In this paper, we study the secrecy provisioning downlink transmission in an aerial-assisted network, where the unmanned aerial vehicle (UAV) serves as an aerial platform to provide secure transmission for the mobile users (MUs) with coexist of Internet of Things (IoT) nodes (INs). Specifically, secure transmission is required for MUs to combat eavesdropping attacks and a desired successful transmission probability should be ensured for INs to receive the public instruction massages. To improve the secrecy rates (SRs) for MUs, we consider an eavesdropper-free area, i.e., protected zone, surrounding the UAV. With non-orthogonal multiple access (NOMA) for MUs, the power allocation to each MU is optimized to maximize the minimum secrecy rate of MUs within the protected zone, under the constraints of successful receiving probability requirements for INs. To solve this problem, we first prove that the max-min SR can be obtained when SRs of all users are equal, and then a dichotomy-based successive power allocation policy is proposed. Numerical results show that higher max-min secrecy rate can be achieved by our proposed power allocation policy than the traditional policy.

To combat the pilot spoofing attack in non-orthogonal multiple access (NOMA) systems, we propose a double channel training scheme in this paper. Specifically, we consider two users in each cluster and both users send the training sequence in the first uplink training phase, while one of them keeps silent in the second phase. By exploiting channel estimation results in the two phases, more accurate legitimate channel estimation can be obtained by removing the contamination from the eavesdropping channel. Thus, the pilot spoofing attack can be mitigated effectively. We then analyze the achievable downlink secrecy rate with matched filter precoding scheme. Simulation results demonstrate that the achievable secrecy rate can be improved dramatically with the proposed scheme even under very strong pilot attack power.

Spectrally efficient frequency division multiplexing (SEFDM) is a promising non-orthogonal multi-carrier technique to improve spectral efficiency, by compressing the inter-carrier interval relative to orthogonal frequency division multiplexing (OFDM) systems. However, by breaking the orthogonality among subcarriers, the self- introduced inter-carrier interference (ICI) severely restrains the achievable transmission rate and poses great challenges in designing the receiver with ICI cancellation. In this paper, we first characterize the statistical distribution of ICI and then derive a closed-form expression of the signal-to- interference-plus-noise ratio (SINR). After that, an efficient time-domain ICI mitigation approach is proposed to improve the achievable SINR and the spectral efficiency of the SEFDM system. Numerical results verify the analytical expressions for the cumulative distribution function (CDF) of ICI and the achievable SINR. In addition, it is shown that the spectral efficiency can be significantly improved by adopting our proposed ICI mitigation approach.

Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks under system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and effectiveness of our proposed model.

A joint user clustering and subcarrier allocation scheme for downlink non-orthogonal multiple access (NOMA) systems is examined in this paper. We propose the scheme for downlink NOMA systems which maximizes the achievable diversity order. The closed-form expression of the outage probability of the worst-performance user in the worst case is derived and validated by simulations. Numerical results show that the proposed scheme can achieve the same diversity order as the exhaustive searching scheme.

In this paper, we investigate physical layer security in a two-tier heterogeneous network with sub-6 GHz massive multi-input multi-output (MIMO) macro cells and millimeter wave (mmWave) small cells. By considering pilot attacks from the eavesdroppers, we analyze the coverage and secrecy performance using stochastic geometry. For the sub-6 GHz tier, we show that increasing the number of BS antennas is more effective than increasing BS density in improving the coverage performance, whereas densifying BS is more effective for security enhancement. For the mmWave tier, we first derive the success probability of beam alignment based on a beam sweeping-based channel training model. It is shown that the mmWave tier may outperform the sub-6 GHz counterpart in terms of both coverage and secrecy through densifying the base stations. Our results also reveal that the mmWave small cell can provide better coverage performance in the high transmission rate region, and can achieve higher security in the low redundant rate region, which reveals the advantage of using mmWave for secure communication. Numerical results verify the analysis.

In this paper, the impact of adaptive smarter eavesdroppers on the secrecy performance of a multi-antenna cellular network is investigated. The eavesdroppers can act as either passive eavesdroppers or active jammers based on their distance to the active base stations (BSs). To analyze the secrecy performance, the BSs, cellular users, and eavesdroppers are modeled as independent Poisson point processes. The closed-form expressions of the connection outage probability and lower bound of the secrecy outage probability are derived using the stochastic geometry approach. Following that, the conditions under which the eavesdroppers can act as active jammers are obtained. Finally, the optimal power allocation between artificial noise and information signal and the secrecy code rate at each BS, as well as the guard zone of the eavesdroppers are obtained using the Stackelberg game approach, where the eavesdroppers are modeled as the leader and the BSs as the follower. The Stackelberg equilibrium is obtained through the proposed iterative algorithm. Numerical results verify the theoretical analysis and show that the secrecy performance can be degraded severely by the adaptive eavesdroppers.

In this paper, the security issue in a multi- input-single-output (MISO) system in the presence of multiple randomly distributed eavesdroppers (Eves) is investigated. The Eves are distributed according to a Poisson Point Process (PPP) and each of them can operate in either a half-duplex (HD) or full-duplex (FD) mode. For a FD-mode Eve, it can overhear the secret information transmission and send some jamming signals simultaneously to degrade the reception of Bob. Based on the stochastic geometry method, we first derive the cumulative distribution function (cdf) expressions of the signal-to-interference-plus- noise ratio (SINR) for Bob and Eves. Following that, a lower bound of the intercept probability is obtained. Some important properties are derived, which provide very useful insights. Finally, the optimal power allocation of Alice and the optimal fraction of FD-mode Eves are solved by using a game-theoretical approach. Simulation results are shown to validate the theoretical analysis.

In this paper, the security in a device-to-device (D2D) spectrum sharing network is investigated. The cellular users and eavesdroppers are distributed according to two independent Poisson Point Processes (PPPs), and the positions of the D2D transmitters follow a hard-core point process. Based on the stochastic geometry, we first derive closed-form expressions of the connection and secrecy outage probabilities of a typical user and then analyze the impacts of different parameters. We also derive the connection probability of a D2D user, which is shown to be independent of the parameters of the base station (BS). Following that, we analyze the impact of D2D transmissions on the power allocation at the BS and derive the optimal power allocation solution for the fixed-rate transmission scheme. Simulation results validate the analysis.

False data injection (FDI) attacks are crucial security threats to smart grid cyber-physical system (CPS), and could result in cataclysmic consequences to the entire power system . However, due to the high dependence on open information networking , countering FDI attacks is challenging in smart grid CPS. Most existing solutions are based on state estimation (SE) at the highly centralized control center; thus, computationally expensive. In addition, these solutions generally do not provide a high level of security assurance, as evidenced by recent work that smart FDI attackers with knowledge of system configurations can easily circumvent conventional SE-based false data detection mechanisms. In this paper, in order to address these challenges, a novel distributed host-based collaborative detection method is proposed. Specifically, in our approach, we use a conjunctive rule based majority voting algorithm to collaboratively detect false measurement data inserted by compromised phasor measurement units (PMUs). In addition, an innovative reputation system with an adaptive reputation updating algorithm is also designed to evaluate the overall running status of PMUs, by which FDI attacks can be distinctly observed. Extensive simulation experiments are conducted with real-time measurement data obtained from the PowerWorld simulator, and the numerical results fully demonstrate the effectiveness of our proposal.

In this paper, physical layer security in multi-antenna small-cell networks is investigated, where the multi-antenna base stations (BSs), cellular users, and eavesdroppers are all randomly distributed according to three independent Poisson point processes. To improve the secrecy performance, artificial noise (AN) aided transmission is adopted at each BS. Based on the stochastic geometry, we first derive the closed-form expressions of the connection and secrecy outage probabilities, and then comprehensively analyze the impact of different parameters through asymptotic analysis. It shows that in a low cell-load case, deploying more BSs will improve the connection and secrecy outage performance, and deploying more transmit antennas at each BS will only improve the connection outage performance. For a fixed-rate transmission, the condition under which AN becomes unnecessary is derived. We also derive a semi closed-form expression of the lower bound of the achievable average secrecy rate, which is numerically efficient to evaluate. Finally, we extend the study to a high cell-load case and adopt the zero-forcing beamforming scheme to support multi-user transmission. The connection and secrecy outage probabilities are also analyzed. Moreover, the optimal number of users maximizing the secrecy area spectral efficiency is discussed, and it is shown to be a fixed portion of the number of transmit antennas. Simulation results are presented to validate the theoretical analysis.

In this paper, the secure transmission strategy for a multi-input-single-output multi-eavesdropper system with coexistence of a secure user (Bob) and a normal user (NU) is investigated. The NU and Bob require normal and secure data transmissions, respectively, and thus, the stream for the NU can be exploited to confuse the eavesdroppers. To guarantee the security of Bob, artificial noise (AN) is also deliberately injected into the null space of Bob and NU. The power allocation among Bob, NU, and AN, as well as the wiretap code rates, is jointly optimized to maximize the effective secrecy throughput (EST), under the average throughput constraint of the NU and statistical channel state information (CSI) of eavesdroppers. Both non-adaptive and adaptive transmission schemes are proposed, based on the statistical and instantaneous CSI of the legitimate channels, respectively. An alternative optimization algorithm is proposed to obtain the optimal parameters. It is proved that the EST is a quasi-concave function of the secrecy rate and the power allocated to Bob, and for fixed wiretap code rates, the optimal power allocation is derived in a closed-form expression. Numerical results show that the EST increases with the increase in transmitting power and the number of transmit antennas, and decreases with the increasing throughput constraint of the NU. Improved EST can be achieved through injecting AN and concurrent transmission of Bob and NU.

In this paper, the security of multi-input single- output (MISO) amplify-and-forward relaying network with untrustworthy relay nodes is considered, where the untrustworthy nodes can help to forward the received signal and they may also try to decode such information, which can be regarded as potential eavesdroppers (Eves). To deal with such kind of smarter Eves, relay selection is adopted and both the maximal-ratio transmission (MRT) scheme and the zero-forcing beamforming (ZFBF) scheme are used at the multi-antenna base station (BS). To achieve a positive secrecy capacity, the destination also injects intended jamming signal to confuse the decoding of the untrustworthy nodes. Either approximation or lower bound of the secrecy outage probability is derived in closed form. It is shown that the proposed partial relay selection scheme approaches the optimal relay selection scheme. In particular, for the ZFBF scheme, when the number of antennas is large enough, increasing the number of relay nodes will increase the diversity order and thus improves the secrecy performance. Simulation results validate the effectiveness of the scheme and correctness of analysis.

In this paper, we study a wireless-powered cooperative relaying system which consists of a source node (SN), a relay node (RN) and a destination node (DN). The RN has no embedded power supply, thus it needs to harvest energy from the radio signal transmitted by a power beacon (PB) which is responsible for charging the RN before forwarding the information to the DN. In addition, we assume that the RN possesses a buffer and can temporarily store the information received from the SN. Based on this assumption, we propose an adaptive transmission scheme in which the system adaptively switches between two transmission modes, namely the SN information transmission (SN-IT) mode and the RN harvest-and-transmit (RN-HAT) mode. The optimal mode adaptation method that maximizes the throughput of the system is obtained for different system setups and the throughput of the system is obtained in closed-form expressions. Numerical results are provided to verify the analytical expressions. It is shown that the throughput of the wireless-powered cooperative relaying system can be improved by using the proposed adaptive transmission scheme.

In the hierarchical control paradigm of a smart grid cyber-physical system, decentralized local agents (LAs) can potentially be compromised by opportunistic attackers to manipulate electricity prices for illicit financial gains. In this paper, to address such opportunistic attacks, we propose a Dirichlet-based detection scheme, where a Dirichlet-based probabilistic model is built to assess the reputation levels of LAs. Initial reputation levels of the LAs are first trained using the proposed model, based on their historical operating observations. An adaptive detection algorithm with reputation incentive mechanism is then employed to detect opportunistic attackers. We demonstrate the utility of our proposed scheme using data collected from the IEEE 39-bus power system with the PowerWorld simulator.

In this paper, the security aspect of an amplify-and-forward relaying network with untrusted relay nodes is considered. The untrusted nodes can help to forward the received signal and they may also try to decode such information, which can be regarded as potential eavesdroppers (Eves). To deal with such a challenging issue, a successive relaying scheme is adopted, where the multi-antenna source transmits to two selected nodes alternately, and the conventional detrimental inter-relay interference is used to jam the untrusted nodes without external helpers. Considering different complexity requirements, several relay selection schemes are proposed, and the closed-form expressions of the lower bound of secrecy outage probability are derived accordingly. To obtain some insights for the network design, an asymptotic analysis is also given, which shows that the maximum secrecy diversity order of N-1 can be achieved, where N is the number of the untrusted relays. Moreover, the spectral efficiency is improved dramatically with the successive relaying scheme. Simulation results show that the proposed max-min scheme has almost the same performance as that of the optimal one, and the theoretical results match well with the simulation results.

Accurate focusing of highly squinted azimuth-variant bistatic synthetic aperture radar data is a difficult issue due to the relatively large range migration, sensibility of the higher order terms, and the inherent geometric variance. To accommodate for this problem, extended azimuth nonlinear chirp scaling algorithm is investigated in this letter. First, range-azimuth coupling is mitigated through a linear range walk correction operation, and then, bulk secondary range compression is implemented to compensate the residual range cell migration and cross-coupling terms. Following which, the characteristics of the azimuth-dependent quadratic and cubic phase terms are analyzed, and modified scaling coefficients are derived by adopting higher order approximation and incorporating the azimuth-dependent range offset caused by the inherent geometric configuration. Compared with traditional nonlinear chirp scaling method, large azimuth depth of focusing can be realized without changing the overall procedure. Simulation results validate the effectiveness of the proposed algorithm.

High-resolution imaging for squint azimuth-variant bistatic synthetic aperture radar system is a challenging task due to the existence of the spatial variance of range cell migration (RCM) and Doppler frequency modulation (FM) rate. To address this problem, azimuth nonlinear chirp scaling (ANLCS) is investigated in this letter. First, linear range walk is removed and then ANLCS is applied in the range frequency azimuth time domain to correct the azimuth-variant RCMs and to equalize the different FM rates. Taking the 2-D variance caused by the azimuth-variant configuration into consideration, a new perturbation function is derived based on the bistatic geometry. Using method of series reversion, a close form of range-azimuth coupling is obtained and corrected in the range Doppler domain by an interpolation-free operation. Incorporated with the secondary range compression, this method leads to a more accurate focusing for azimuth-variant bistatic configurations, even with high squints. Simulation results validate the effectiveness of the method.

In this letter, we present the mode of forward-moving rotor synthetic aperture radar (ROSAR) to achieve 3-D imaging for the front area of the low-altitude aircraft. First, the geometric model is given, and the signal property is analyzed. Based on which, spatial offsets and coupling in the azimuth and along-track directions are corrected in the frequency domain, and then, the forward-moving ROSAR is simplified as a “stop-and-go” mode in the along-track direction. After 2-D imaging in the range-azimuth direction, a 3-D image can be obtained by range-along-track focusing with the improved range-Doppler algorithm. Moreover, azimuth depth of focusing is also given to maintain the imaging quality. Finally, simulation results prove the feasibility and effectiveness of the proposed method for this new imaging mode.