With the emergence of fluid antenna (FA) in wireless communications, the capability to dynamically adjust port positions offers substantial benefits in spatial diversity and spectrum efficiency, which are particularly valuable for mobile edge computing (MEC) systems. Therefore, we propose an FA-assisted MEC offloading framework to minimize system delay. This framework faces two severe challenges, which are the complexity of channel estimation due to dynamic port configuration and the inherent non-convexity of the joint optimization problem. Firstly, we propose Information Bottleneck Metric-enhanced Channel Compressed Sensing (IBM-CCS), which advances FA channel estimation by integrating information relevance into the sensing process and capturing key features of FA channels effectively. Secondly, to address the non-convex and high-dimensional optimization problem in FA-assisted MEC systems, which includes FA port selection, beamforming, power control, and resource allocation, we propose a game theory-assisted Hierarchical Twin-Dueling Multi-agent Algorithm (HiTDMA) based offloading scheme, where the hierarchical structure effectively decouples and coordinates the optimization tasks between the user side and the base station side. Crucially, the game theory effectively reduces the dimensionality of power control variables, allowing deep reinforcement learning (DRL) agents to achieve improved optimization efficiency. Numerical results confirm that the proposed scheme significantly reduces system delay and enhances offloading performance, outperforming benchmarks. Additionally, the IBM-CCS channel estimation demonstrates superior accuracy and robustness under varying port densities, contributing to efficient communication under imperfect CSI.

As an emerging communication technology, fluid antenna (FA) offers remarkable diversity and multiplexing gains due to its port mobility, which significantly reduces transmission delays in communication processes. This capability makes FA a promising solution for enhancing mobile edge computing (MEC) by optimizing communication delay. This paper establishes an FA-aided MEC offloading architecture and proposes a game theory-assisted multi-agent deep reinforcement learning (DRL) scheme to minimize the system delay of MEC. We aim to address the joint optimization problem of FA port selection, beamforming, user transmit power design, and MEC server computation resource allocation. However, the dynamic nature of FA ports and the variability of the associated large number of parameters introduce significant challenges, such as non-convexity and high dimension, in the optimization problem. In this paper, we employ game theory to reduce the dimension of the optimization variables by modeling the power control problem among multiple users as a non-cooperative game. Therefore, we propose a multi-agent deep deterministic policy gradient (MADDPG) algorithm, featuring two types of agents that collaboratively solve the problem. Simulation results validate the effectiveness of the proposed scheme, achieving 19.1-65.8% lower delays than benchmarks in MEC efficiency across all scenarios.

With the surge in wireless data traffic, integrating millimeter-wave (mmWave) technology into vehicular networks enables high-speed communication. Meanwhile, the rising demand for secure wireless communication drives the use of reconfigurable intelligent surfaces (RIS) to enhance physical layer security (PLS) through intelligent channel control. This paper investigates PLS approaches in multi-RIS-assisted mmWave vehicular communication under stochastic geometry architecture. Taking the dynamically changing and random nature of vehicular network topologies into account, we propose a vehicular network association scheme for a typical vehicle. In this scheme when the quality of the direct link deteriorates due to obstacles or other factors, RIS-assisted communication ensures a more stable connection. By leveraging stochastic geometry theory, a tractable analytical framework is established to evaluate the secrecy performance of the downlink transmission comprehensively. Specifically, the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) are derived. Simulation results demonstrate that introducing RIS into vehicular networks and utilizing the proposed association scheme can significantly improve the security of vehicular networks.

Federated learning (FL) is a promising approach that effectively and securely harnesses the vast amounts of data generated by the rapid proliferation of internet-connected devices. In FL, the transmission of model parameters over wireless channels plays a pivotal role in determining system performance. To optimize the wireless environment and boost communication efficiency, we present a novel FL beamforming design scheme that integrates multi-sector beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with over-the-air computation (AirComp). The scheme leverages the waveform superposition property of wireless signals, using AirComp to rapidly aggregate the global model in FL. Additionally, the scheme utilizes BD- RIS to flexibly manip-ulate communication beams, improving user channel conditions and further reducing model aggregation errors. Specifically, we evaluate the impact of this design on FL systems and derive an upper limit on the gap between training loss and optimal loss. To minimize this gap, we formulate a joint optimization problem of BD- RIS passive beamforming and base station receive beamforming, and we propose an optimization algorithm based on successive convex approximation (SCA) and block coordinate descent (BCD) to solve it. Simulation results confirm that our de-sign significantly enhances user channel conditions and improves FL performance, with the benefits becoming more pronounced as the number of BD- RIS reflecting elements increases.

In remote or disaster areas, where terrestrial networks are difficult to cover and Terrestrial Edge Computing (TEC) infrastructures are unavailable, solving the computation computational offloading for Internet of Vehicles (IoV) scenarios is challenging. Current terrestrial networks have high data rates, great connectivity, and low delay, but global coverage is limited. Space–Air–Ground Integrated Networks (SAGIN) can improve the coverage limitations of terrestrial networks and enhance disaster resistance. However, the rising complexity and heterogeneity of networks make it difficult to find a robust and intelligent computational offload strategy. Therefore, joint scheduling of space, air, and ground resources is needed to meet the growing demand for services. In light of this, we propose an integrated network framework for Space-Air Auxiliary Vehicle Computation (SA-AVC) and build a system model to support various IoV services in remote areas. Our model aims to maximize delay and fair utility and increase the utilization of satellites and Autonomous aerial vehicles (AAVs). To this end, we propose a Deep Reinforcement Learning algorithm to achieve real-time computational computational offloading decisions. We utilize the Rank-based Prioritization method in Prioritized Experience Replay (PER) to optimize our algorithm. We designed simulation experiments for validation and the results show that our proposed algorithm reduces the average system delay by 17.84%, 58.09%, and 58.32%, and the average variance of the task completion delay will be reduced by 29.41%, 48.74%, and 49.58% compared to the Deep Q Network (DQN), Q-learning and RandomChoose algorithms.

Despite narrow beams with strong anti-interception capabilities, terahertz communications still face eavesdropping risks in short-range indoor networks. This paper investigates physical-layer security of downlink terahertz communications for indoor three-dimensional (3D) networks comprised of a large number of access points (APs), users, human blockages, and eavesdroppers. We propose two different artificial noise (AN)-assisted terahertz secure transmission schemes, namely the full-AN (F-AN) scheme and partial-AN (P-AN) scheme, under the nearest line-of-sight association (NLA) strategy. The F-AN scheme involves full APs emitting AN to deteriorate the reception of eavesdroppers, and the P-AN scheme selects only those APs with blocked links to the typical user to emit AN based on the unique blocking feature of terahertz. We first obtain the expression for association probability. Then, we determine the eavesdropping region covered by the 3D beam on the ground. We derive the connection outage probability and secrecy outage probability for the two schemes by calculating the Laplace transform of aggregate interference. Our results provide interesting insights into how the secrecy performance is influenced by various system parameters, including the densities of APs and blockages. Moreover, we show that the P-AN scheme outperforms the F-AN scheme regarding the average number of perfect links per unit area.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

Millimeter wave (mmWave) Device-to-Device (D2D) communication networks suffer high path loss and dynamic physical obstructions. Meanwhile, eavesdroppers can intercept confidential information by residing in the main or side lobe of the transmission beam. Fortunately, multiple distributed Reconfigurable Intelligent Surfaces (RISs) offer a valuable approach to support mobile D2D devices, mitigating blocking effects and enhancing data security. In this paper, we propose a deep reinforcement learning (DRL) based communication scheme for the multi-RIS aided dynamic mmWave D2D networks, which aims to maximize the total secrecy data volume of D2D users over each service period by jointly optimizing the RIS resource allocation and multi-RIS phase shift design. To implement the intelligent collaboration of the RISs, we design the DRL approach with a nested structure. Specifically, we adopt a proximal policy optimization (PPO) network with discrete actions to realize the RIS-User association. Subsequently, we integrate the multi-agent PPO (MAPPO) framework to derive the phase shift design, containing intricate dynamic competition and cooperation among RIS agents. In addition, we divide the RIS into multiple subarrays, each sharing the same reflection coefficient. This approach controls more RIS phases while ensuring training stability. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the secrecy performance of dynamic mmWave D2D networks.

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network.

The combination of millimeter-wave (mmWave) and massive MIMO techniques can fulfill high data rate requirements for vehicular networks. However, due to the elevated path loss and severe blocking effects in mmWave propagation, the downlink data service of vehicles will seriously deteriorate. Fortunately, reconfigurable intelligent surface (RIS) can serve as a single-user relay to mitigate individual performance degradation without additional power consumption. In this paper, we propose a deep reinforcement learning (DRL)-based joint user schedule and RIS-User pairing scheme for the dynamic mmWave vehicular network to alleviate the blocking effects and maximize the total transmission data volume while ensuring the quality of service (QoS) for all target vehicles. In this scheme, each target vehicle has a distinct QoS constraint called minimum service data volume, which is a long-term and posterior optimization problem. Thus, QoS constraints are introduced in the reward design of the DRL algorithm, and the problem of high-dimensional action spaces is addressed by utilizing two nested Dueling Double-DQN (D-D3QN) networks. Simulation results demonstrate the superiority of our scheme in mmWave vehicular networks.

Unmanned aerial vehicles (UAVs), known for their high flexibility and maneuverability, are regarded as the aerial platforms of future integrated sensing and communication (ISAC) networks. The communication and sensing functions of ISAC share the same spectrum and signal waveform, which often results in communication information being embedded within the sensing waveforms, thereby increasing the risk of information leakage. To enhance the security of UAV-assisted ISAC, we propose a beamforming strategy based on the mutual cooperation between communication and sensing. Specifically, by utilizing the sensing function to process echo signals, we estimate the positions of potential eavesdroppers and obstacles, which supports subsequent obstacle avoidance trajectory planning and physical layer security design. To ensure the transmission secrecy, we introduce artificial noise into the system. By designing the UAV transmit beamforming and the covariance matrix of the artificial noise, we formulate an optimization problem that aims to minimize the signal-to-noise ratio (SNR) received by the eavesdropper. To address this non-convex optimization problem, we propose an optimization algorithm that combines Dinkelbach's transform and semidefinite relaxation (SDR). Simulation results demonstrate that the SNR of eavesdropper remains at a low level throughout the flight of UAV, validating the effectiveness of the proposed scheme.

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks.

The massive data transmission in vehicular networks has given rise to the demand for high-capacity communication and information security. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technology to escalate the communication capacity of multiple vehicle users (VUs), and design artificial noise (AN)-based secure transmission schemes for this new NOMA-mmWave communication architecture. The AN beamforming matrix is derived from the mmWave discrete angular channel model to fully exploit the characteristics of mmWave propagation and facilitate the analysis. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the analytical expressions for the performance metrics. Numerical results demonstrate that the proposed scheme can effectively improve the secrecy performance of the NOMA-mm Wave vehicular communications.

Mobile edge computing (MEC) offers promising solutions for various delay-sensitive vehicular applications by providing high-speed computing services for a large number of user vehicles simultaneously. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading from the user vehicles to the MEC server at the base station, the physical layer security (PLS) technology is leveraged, where a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate offloading of the user vehicle. We formulate a joint optimization of the transmit power, the computation resource allocation and the selection of jammer vehicles in each NOMA cluster, with the objective of minimizing the system energy consumption while subjecting to the computation delay constraint. Due to the dynamic characteristics of the wireless fading channel and the high mobility of the vehicles, the joint optimization is formulated as a Markov decision process (MDP). Therefore, we propose an asynchronous advantage actor-critic (A3C) learning algorithm-based energy-efficiency secure offloading (EESO) scheme to solve the MDP problem. Simulation results demonstrate that the agent adopting the A3C-based EESO scheme can rapidly adapt to the highly dynamic VEC networks and improve the system energy efficiency on the premise of ensuring offloading information security and low computation delay.

Millimeter wave (mmWave) with abundant spectrum resources can realize high-rate communications in vehicular networks. However, the mobility of vehicles and the blocking effect of mmWave propagation bring new challenges to communication security. Cooperative communication is envisioned as a promising physical layer security (PLS) approach to enhance the secrecy performance, but it will induce extra energy consumption of vehicles. This paper proposes a deep recurrent reinforcement learning (DRRL)-based energy-efficient cooperative secure transmission scheme in mmWave vehicular networks, where eavesdropping vehicles attempt to intercept the multi-user downlink communications. We jointly design the mmWave beam allocation, the cooperative nodes selection, and the transmit power of vehicles. Specifically, the mmWave base station selects idle vehicles as relays to overcome the severe blocking attenuation of legitimate transmissions and controls the transmit power to reduce energy consumption. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropping vehicles while the legitimate users are not disturbed. We conduct comprehensive interference analysis for both direct transmission and relay-aided transmission, and derive the theoretical expressions for the secrecy capacity. We then design the Dueling Double Deep Recurrent Q-Network (D3RQN) learning algorithm to maximize the total secrecy capacity subject to the energy consumption constraint. We set the energy consumption punishment mechanism to avoid relay vehicles consuming too much power for forwarding signals. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance while reducing the energy consumption of vehicles.

Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc –based CPS provides a promising architecture due to its decentralized nature and destructive-resistance. A growing number of information leakage events in CPSs and the following serious consequences have aroused ubiquitous concern about information security. In this article, we combine physical layer security solutions and millimeter-wave (mmWave) techniques to safeguard the ad hoc network and investigate the reliability-security tradeoff by taking user demands for the network into account, where eavesdroppers attempt to intercept messages. For the secrecy enhancements, we adopt an artificial noise (AN) assisted transmission scheme, in which AN is employed to create non-cancellable interference to eavesdroppers. The reliability and security are correspondingly characterized by the connection outage probability and secrecy outage probability, and their analytical expressions of them are attained through theoretical analysis for the purpose of the tradeoff issue discussion. Our results reveal that secrecy performance in mmWave ad hoc networks gains significant improvement through the use of AN. It also shows that given total transmit power, there exists a tradeoff between reliability and security to achieve optimal outage performance.

Terahertz communication with abundant spectrum resources is envisioned as the key technology of 6G. Despite its narrow beam, terahertz transmission is still vulnerable to eaves-dropping attacks in indoor scenarios. In this paper, we propose a blockage feature-based artificial noise scheme to safeguard the indoor network in the presence of multiple access points (APs), users, and eavesdroppers. Those APs with blocked links to the typical user are selected to emit artificial noise to deteriorate the reception of eavesdroppers. Thus, communication security is ensured without escalating the instability of legitimate connections caused by the small coverage nature of terahertz beams. By comprehensively considering the propagation characteristics of terahertz, such as the three dimensions narrow beam and the human blocking effect, we derive the theoretical expressions of the connection outage probability, the secrecy outage probability, and the average number of perfect links per unit area. Numerical results demonstrate that the proposed scheme outperforms the traditional schemes in terms of connection stability and secrecy performance.

Massive flood damage has garnered a lot of social attention. Due to the tension between the strong demand for generalized models and the constrained capabilities of edge devices for hydrological surveillance, this study proposes an accessible distributed hydrological surveillance (HS) and computing system with integrated end-edge-cloud (iEEC) architecture to address the issue. In order to increase the inference efficiency of the edge servers (ES), we first develop a HS model with multiple exits, aiming to exploit its network structure and inference strategy. Then, using a collaborative scheduling algorithm, we construct the iEEC pathway to decide whether to undertake edge inference or cloud invocation. With a prototype system and a simulation tool, we eventually performed a numerical analysis of the system at various scales. The accuracy reached 94.3 %, the speed reached 30.3 frames per second (FPS), and it can better handle the occurrence of hard instances compared to state-of-the-art (SOTA) approaches.

High-speed computation resources are provided by mobile edge computing (MEC) to boost various delay-sensitive vehicular applications. However, compared to computing tasks locally, the MEC approach consumes extra energy in the offloading process. In this paper, an asynchronous deep reinforcement learning-based energy-efficient secure offloading (EESO) is proposed to enhance the energy efficiency and security of the vehicular edge computing (VEC) network in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading process of the information, a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate user vehicle. We minimize the system energy consumption with the computation delay constraint by jointly optimizing the transmit power, the computation resource allocation, and the selection of jammer vehicles in each NOMA cluster. Then we adopt an asynchronous advantage actor-critic (A3C) learning algorithm to solve the optimization problem. With proper training, the A3C-based EESO scheme can reduce the system energy consumption and improve offloading security.

Millimeter wave (mmWave) technology provides abundant high-capacity channel resources for vehicular communications. However, the mobility of vehicles and the blocking effect of mmWave propagation brings new challenges to communication security. From the perspective of cooperative secure communication, this paper proposes a deep reinforcement learning (DRL)-based joint relay and jammer selection scheme in mmWave vehicular networks. The mmWave base station selects idle vehicles as relay transmission nodes to overcome the severe blocking attenuation of the multi-user downlink legitimate transmissions. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropper while the users are not disturbed. We utilize the asynchronous advantage actor-critic (A3C) learning algorithm to optimize the cooperative vehicle selection with the objective of maximizing the total secrecy capacity. Besides, we set the secrecy rate punishment mechanism to guarantee the secrecy performance of each vehicle. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance.

Despite the high directionality of antennas in terahertz communication, there remains a risk of confidential message interception when eavesdroppers are within the beam coverage area. This paper proposes a blockage-based cooperative jamming scheme to enhance the security of terahertz communication. Due to significant signal attenuation caused by blockages in the terahertz frequency band, we select idle users with blockages between them and the typical user in the indoor three-dimensional (3D) space to act as cooperative jammers. Thus, the jamming signal can deteriorate the reception of eavesdroppers while effectively minimizing interference to the typical user. Taking into account the influence of terahertz channel characteristics, blockage, and 3D antenna model, we derive analytical expression for the secrecy outage probability (SOP). Besides, we analyze the effects of access point (AP) density, blockage density, and user idle factor on network performance. Our results demonstrate that the blockage-based cooperative jamming scheme effectively improves the secrecy performance of the terahertz network.

Internet of Things (IoT) applications have penetrated into all aspects of human life. Millions of IoT users and devices, online services, and applications combine to create a complex and heterogeneous network, which complicates the digital identity management. Distributed identity is a promising paradigm to solve IoT identity problems and allows users to have soverignty over their private data. However, the existing state-of-the-art methods are unsuitable for IoT due to continuing issues regarding resource limitations for IoT devices, security and privacy issues, and lack of a systematic proof system. Accordingly, in this article, we propose SmartDID, a novel blockchain-based distributed identity aimed at establishing a self-sovereign identity and providing strong privacy preservation. First, we configure IoT devices as light nodes and design a Sybil-resistant, unlinkable, and supervisable distributed identity that does not rely on central identity providers. We further develop a dual-credential model based on commitment and zero-knowledge proofs to protect the privacy of sensitive attributes, on-chain identity data, and linkage of credentials. Moreover, we combine the basic credential proofs to prove the knowledge of solutions to more complex problems and create a systematic proof system. We go on to provide the security analysis of SmartDID. Experimental analysis shows that our scheme achieves better performance in terms of both credential generation and proof generation when compared with CanDID.

Millimeter-wave (mmWave) can provide abundant spectrum resource in vehicular communication networks. Nevertheless, due to the high path-loss and blocking effects in mmWave propagation, and high mobility of vehicles, downlink services for vehicles would be seriously degraded. In this paper, we firstly propose a deep reinforcement learning-based joint beam allocation and relay selection (JoBARS) scheme to mitigate blocking effects and optimize the total transmission rate of the vehicular network, where the mmWave base station (mmBS) provides multi-user services. When downlinks are blocked, the mmBS can select appropriate idle vehicles as relay nodes to enhance service quality from a global perspective. We set the rate punishment restriction in JoBARS scheme to guarantee each vehicle can obtain high-quality service. Besides, a relaying incentive mechanism (RIM) is proposed to avoid vehicles being overly selected for relaying and ensure that relay vehicles have a higher chance of being served in the next round. We demonstrate that JoBARS scheme can effectively enhance the total transmission rate while alleviating transmission outages caused by severe propagation attenuation of mmWave signals. Compared with Greedy Selection scheme, the total rate and average connection probability of vehicles under JoBARS scheme are nearly 17% and 14% higher when blocking effects are severe.

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

A lack of well-designed security solutions within the millimeter-Wave (mmWave) cellular vehicle-to-everything (V2X) communications significantly impedes the development of applications within the intelligent transportation system. Cooperative jamming is envisioned as a potential technology that can enhance physical layer security performance for plane networks by selectively choosing jammers from the perspective of the legitimate receiver. We propose a blockage-and-power-based jammer selection strategy to address potential security pitfalls in a mmWave cellular V2X network. With the help of jammers whose interference power falls within the acceptance range of legitimate receivers, transmission confidentiality is secured simultaneously without escalating the instability of connections caused by the time-varying nature of V2X networks. We derive the theoretical expression of secrecy outage probability and secrecy throughput based on our preliminary analysis of association probability from the stochastic geometry approach. Numerical results demonstrate that the proposed secure transmission scheme outperforms other cooperative jamming schemes in terms of secrecy throughput.

The full-duplex millimeter-wave communication has drawn significant attention for its rich spectrum resources and high spectrum efficiency characteristics. However, due to the information leakage during transmission, secure threats in the full-duplex systems still exist. In this paper, we propose a full-duplex jamming based secure transmission scheme, where the instantaneous channel state information of eavesdropping channel is unknown. We study the optimal design of hybrid beamforming and power allocation jointly with secrecy outage probability constraint. Our results reveal that joint optimization highly facilitates the secrecy performance of full-duplex mmWave communication, without any self-interference limitations in the multiple-antenna scenarios.

Millimeter wave (Mmwave) communication can realize high rate service for the upcoming Internet of Things (IoT) networks. Although directional multiantenna gains can help enhance security, randomly distributed eavesdroppers can still intercept confidential messages by residing in both the main-lobe and side-lobe areas of the beam signal. Considering the unique propagation features of mmWave, this article explores the potential of physical layer security in mmWave multiple-input–multiple-output (MIMO) systems. We propose an artificial noise (AN)-aided capacity threshold on–off secure transmission scheme to resist the eavesdropping threat. Taking into account the influence of mmWave channel characteristics, random blockage, and multiantenna gains, we first derive the closed-form expressions of transmission probability (TP) and secrecy outage probability (SOP) in a noncolluding eavesdropping scenario. Then, the lower bound of SOP with AN and closed-form expression of SOP without AN is derived in a colluding eavesdropping scenario. Theoretical analysis evaluates the impacts of various system parameters on secrecy performance and verifies the effects of AN interference on inhibiting side-lobe eavesdropping. Simulation results validate the theoretical results and indicate that the combination of capacity threshold on–off transmission scheme, AN interference, and multiantenna directional gains can effectively reduce the security threats of mmWave MIMO systems. Besides, the optimal power allocation ratio of AN in noncolluding scenarios is demonstrated and its rule is summarized, which depends on whether legitimate communication links are in blockage.

In this paper, we investigate physical-layer security of the uplink millimeter wave communications for a cellular vehicle-to-everything (C-V2X) network comprised of a large number of base stations (BSs) and different categories of V2X nodes, including vehicles, pedestrians, and road side units. Considering the dynamic change and randomness of the topology of the C-V2X network, we model the roadways, the V2X nodes on each roadway, and the BSs by a Poisson line process, a 1D Poisson point process (PPP), and a 2D PPP, respectively. We propose two uplink association schemes for a typical vehicle, namely, the smallest-distance association (SDA) scheme and the largest-power association (LPA) scheme, and we establish a tractable analytical framework to comprehensively assess the security performance of the uplink transmission, by leveraging the stochastic geometry theory. Specifically, for each association scheme, we first obtain new expressions for the association probability of the typical vehicle, and then derive the overall connection outage probability and secrecy outage probability by calculating the Laplace transform of the aggregate interference power. Numerical results are presented to validate our theoretical analysis, and we also provide interesting insights into how the security performance is influenced by various system parameters, including the densities of V2X nodes and BSs. Moreover, we show that the LPA scheme outperforms the SDA scheme in terms of secrecy throughput.

Millimeter-wave(Mmwave) networks with high directional antennas have enhanced security. However, eavesdroppers can still intercept confidential messages by residing in both signal main-lobe and side-lobe areas. This paper investigates the secrecy performance of artificial-noise (AN)-aided transmission in mmWave systems under the capacity-threshold on-off scheme in the presence of randomly distributed eavesdroppers, which utilizes AN to resist eavesdroppers in the side-lobe area. Considering the effects of mmWave channel characteristics, blockages, and directional antenna arrays, we derive the transmission probability (TP) and closed-form expression of secrecy outage probability (SOP) in the non-colluding eavesdropping scenario. What's more, we derive the analytical expression of SOP in the colluding eavesdropping scenario and its lower bound. Specifically, we characterize the impacts of various system parameters on the secrecy performance and verify the contribution of AN-jamming to inhibiting side-lobe information leakage, meanwhile, the optimal power allocation of AN is analyzed in non-colluding scenarios. Besides, our results reveal that with the narrower main beam and higher antenna gain, the secrecy performance is enhanced significantly. The analytical and numerical results show that the capacity-based transmission scheme with AN-jamming can effectively improve the secrecy performance of mmWave systems.

Exploiting relays in millimeter wave (mmWave) systems is an effective way to extend the communication coverage and overcome the blockage problem. This paper comprehensively studies secure transmissions in mmWave decode-and-forward (DF) relay systems. Depending on the overlapped resolvable paths between the main channel and the wiretap channel in each transmission stage, we consider three eavesdropping scenarios, namely two-stage eavesdropping (TSE), single-stage eavesdropping (SSE) and no eavesdropping (NE). We investigate secrecy performance and optimal parameter design of these eavesdropping scenarios under the same codeword transmission (SCT) scheme and the different codewords transmission (DCT) scheme, where source and relay utilize same codeword or different codewords. Specifically, we derive closed-form expressions for connection probability and secrecy outage probability, and then give solution to the secrecy throughput maximization problem. Furthermore, we investigate the effectiveness of the artificial noise (AN) by evaluating the secrecy performance of AN assisted transmissions. Numerical results are provided to verify our theoretical analysis. Our results give insights into the secure transmission scheme selection and the impact of various parameters, such as number of antennas, power allocation between source and relay, number of overlapped paths, and distances between different nodes, on the secrecy performance of the mmWave relay system.

Mm-wave offers a sensible solution to the capacity crunch faced by 5G wireless communications. This paper comprehensively studies physical layer security in a multi-input single-output mm-wave system, where multiple single-antenna eavesdroppers are randomly located. Concerning the specific propagation characteristics of mm-wave, we investigate two secure transmission schemes, namely maximum ratio transmitting beamforming and artificial noise (AN) beamforming. Specifically, we first derive closed-form expressions of the connection probability for both schemes. We then analyze the secrecy outage probability in both non-colluding eavesdroppers and colluding eavesdroppers scenarios. Also, we maximize the secrecy throughput under a secrecy outage probability constraint, and obtain optimal transmission parameters, especially the power allocation between AN and the information signal for AN beamforming. Numerical results are provided to verify our theoretical analysis. We observe that the density of eavesdroppers, the spatially resolvable paths of the destination and eavesdroppers all contribute to the secrecy performance and the parameter design of mm-wave systems.

Exploiting millimeter wave is an effective way to meet the data traffic demand in the 5G wireless communication system. In this paper, we study secure transmissions under slow fading channels with multipath propagation in millimeter wave systems. Concerning the new propagation characteristics of millimeter wave, we investigate three transmission schemes, namely, maximum ratio transmitting (MRT) beamforming, artificial noise (AN) beamforming, and partial MRT (PMRT) beamforming. We evaluate the secrecy performance by analyzing both the secrecy outage probability (SOP) and the secrecy throughput for each scheme. Particularly, for the AN scheme, we derive a closed-form expression for the optimal power allocation ratio of the information signal power to the total transmit power that minimizes the SOP, as well as obtain an explicit solution on the optimal transmission parameters that maximize the secrecy throughput. By comparing the secrecy performances achieved by different strategies, we demonstrate that the secrecy performance of the millimeter wave system is significantly influenced by the relationship between the legitimate user's and the eavesdropper's spatially resolvable paths, which is different from the wireless systems with statistically independent channel models. In the absence of the common path between the legitimate user and the eavesdropper, MRT beamforming is the best scheme. In the presence of common paths, AN beamforming and PMRT beamforming show their respective superiorities depending on the transmit power and the number of common paths. Numerical results are provided to verify our theoretical analysis.

In this paper, we provide a comprehensive analysis of the multi-antenna transmission in a K-tier downlink heterogeneous cellular network (HCN). We first propose a reliability-oriented mobile access policy with an access threshold, in which each user connects to the strongest base station in terms of the truncated long-term received power. Under this policy, we derive for a random user explicit analytical expressions of the exact out-age probability along with its computational convenient lower and upper bounds. Asymptotic analysis on the outage probabilities shows that introducing the access threshold efficiently improves outage performance. We further investigate the area network throughput from the perspective of outage. Our theoretic analysis and numerical validations show that throughput performance can be enhanced by properly designing the access threshold.

In this paper, we study the secrecy throughput in millimeter wave systems under slow fading channels considering multipath propagation. For the specific propagation characteristics of millimeter wave, we provide transmission scheme designs and a comprehensive secrecy performance analysis. Specifically, we maximize the secrecy throughput under a secrecy outage probability (SOP) constraint through a dynamic parameter transmission scheme, and provide the optimal solution to transmission parameters, including the codeword rate and the power allocation ratio of the information signal power to the total transmit power. We find that the secrecy performance of the millimeter wave system under investigation is significantly influenced by the relationship between spatially resolvable paths of the legitimate user and those of the eavesdropper, which differs from those wireless systems with statistically independent channel. Numerical results are provided to verify our theoretical analysis.

Non-orthogonal multiple access (NOMA) has been recognized as a potential technique to achieve higher spectral efficiency (SE) for future 5G systems. However, the anticipated thousand-fold increase in wireless data traffic urgently calls for new designs of power-efficient communication systems. In this paper, we investigate NOMA from the perspective of energy efficiency (EE) in a multiuser downlink system. Firstly, we generalize an existing cognitive radio (CR) inspired NOMA scheme by introducing multiple antenna techniques and extending the number of primary users to an arbitrary number. Then, we aim at optimizing the EE of this generalized CR inspired NOMA scheme subject to an individual quality of service (QoS) constraint for each primary user, which leads to a non-convex fractional programming problem. For this challenging problem, we propose an efficient algorithm based on the sequential convex approximation (SCA) method. Our numerical results show that NOMA has superior EE performance in comparison with conventional orthogonal multiple access (OMA).

A practical uncoordinated cooperative jamming (UCJ) scheme with multiple single-antenna helpers is proposed in this paper to enhance the physical layer security of single-input-multiple-output (SIMO) wiretap channel. In the scheme, both the intended receiver and the eavesdropper are equipped with multiple antennas and apply the minimum mean-square error (MMSE) combiner. The multiple uncoordinated single-antenna helpers transmit jamming signals independently to confound the eavesdropper, and we focus on power allocation for the helpers to solve the secrecy rate maximization (SRM) problem. Assuming that the statistical channel state information (CSI) concerning the eavesdropper is available, a convex conservative secrecy outage probability (SOP) constraint is derived and then used to solve the SRM problem with DC (difference of convex function) programming method. Furthermore, a bisection-like refinement method is provided to find a quality solution of the SRM problem with the original SOP constraint. Numerical results show that the proposed scheme has good secrecy performance especially when the number of helpers is large.

The use of millimeter wave is an effective way to meet the data traffic demand of the 5G wireless communication system. For the new propagation characteristics of millimeter wave, in this paper, we study the secure transmission with artificial noise under slow fading channels considering multipath propagation in millimeter wave systems. Firstly, when partial eavesdropper's channel state information is known at the transmitter, an artificial noise transmission strategy which depends on directions of the destination's and the eavesdropper's propagation paths is proposed. Then we analyze the secrecy outage probability (SOP) through an on-off transmission scheme. Furthermore, we minimize the SOP subject to a secrecy rate constraint and derive a closed-form optimal power allocation between the information bearing signal and artificial noise. Numerical results are provided to show the superiority of the proposed scheme.