Spread spectrum communication plays a vital role in safeguarding the Internet of Things systems, due to its inherently low probability of interception and anti-jamming capability. However, conventional spread spectrum systems based on pseudorandom sequences are disadvantageous in limited sequence length and deterministic periodicity, making them vulnerable to brute-force attacks. To address these limitations and enhance the randomness of the spread spectrum sequences, a novel wireless channel randomness integrated spread spectrum sequence generation method is proposed in this work. Taking advantages of the intrinsic randomness, temporal variations, and unpredictability of wireless channel fadings, the proposed approach converts the extracted channel features into ordered sequences, which are then used to control the selection of irreducible generating polynomials for spread spectrum sequence generation. The proposed method improves the randomness and secrecy of the integrated spread spectrum sequence. Theoretical analysis and simulation results demonstrate that the proposed sequences not only achieve higher randomness entropy compared to the traditionalm-sequence, but also pass the National Institute of Standards and Technology randomness tests. Furthermore, performance evaluations under various signal-to-interference ratio conditions show improved autocorrelation properties and largely lower bit error rates, validating the effectiveness of the proposed method in improving the anti-jamming capability.

To address the challenges of information leakage, low energy efficiency, and the Doppler effect in mobile Internet of Vehicles (IoV), this paper proposes an enhanced IoV cooperation framework, where privacy information is forwarded by the untrusted relay assisted by unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), which can improve security and energy efficiency. To meet the requirements of green communication, we formulate a secrecy energy efficiency maximization problem by jointly optimizing the transmit power allocation, the relay’s amplification factor, the two-hop RIS phase shift matrices, and the UAV trajectory. Given the non-convex nature of this problem, we introduce an iterative algorithm based on the convex-concave procedure and Dinkelbach’s method to optimize the transmit power and amplification factor. Additionally, we conceive the majorization-minimization (MM) algorithm to optimize the two-hop RIS phase shift matrices, and a designed firefly algorithm-deep deterministic policy gradient (FA-DDPG) algorithm is proposed to obtain the UAV trajectory. Simulation results demonstrate the effectiveness of the proposed scheme in enhancing secrecy energy efficiency. Specifically, compared to the DDPG-only and FA-based schemes, the proposed scheme achieves an improvement of 33.3% and 64.2%, respectively, in secrecy energy efficiency.

Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) is emerging as a key 6G technology for improving spectral efficiency and enabling high-resolution sensing. However,sensing targets within the communication coverage may act as potential eavesdroppers and intercept confidential data. To address this challenge, this paper proposes a sensing-assisted secure beamforming framework to enhance physical-layer security (PLS). First, we design a closed-loop architecture that sequentially performs RIS cascaded CSI estimation, target direction-of-arrival (DoA) estimation, and a Cramér–Rao bound (CRB)-based sensing accuracy evaluation. We then introduce an angular-domain information leakage (ADIL) metric to characterize leakage within the target’s angular uncertainty region. Building on this metric, we formulate a weighted-sum utility to jointly optimize the communication rate and sensing CRB under an ADIL-suppression constraint. To solve the resulting non-convex problem, we develop a penalty dual decomposition (PDD)-augmented alternating optimization (AO) algorithm that iteratively updates the BS beamforming, RIS phase shifts, and sensing time allocation. Convergence and complexity analyses further demonstrate that PDD accelerates AO convergence and mitigates zig-zag updates caused by coupled variables. Simulation results verify that the proposed sensing-assisted secure beamforming scheme effectively suppresses ADIL at eavesdropper angles and enhances PLS. Moreover, the PDD-augmented AO achieves up to a 21.3% improvement in communication rate and a 5.2% reduction in CRB compared with conventional schemes.

This paper investigates a novel RIS-assisted downlink mobile Internet of Vehicles (IoV) network to improve the service quality of long-distance communications. The proposed network leverages unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS), which can increase the signal path, thereby recovering the interrupted links and enhancing the signal strength. Additionally, the Doppler shift is considered in channel modeling to characterize the channel properties of dynamic environments. Aiming at the high-speed demand of 6 G networks, this paper introduces non-orthogonal multiple access (NOMA) technology to serve multiple vehicles and formulates a sum rates (SR) maximization problem. Then, an iterative framework is proposed for joint optimization, adopting the designed adaptive firefly algorithm (FA), semidefinite relaxation (SDR) technology, and deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate that the proposed scheme can significantly enhance SR performance compared with space division multiple access and orthogonal multiple access schemes.

In this paper, we propose a novel physical-layer key generation (PKG) scheme for orthogonal frequency division multiplexing-orbital angular momentum (OFDM-OAM) systems to significantly enhance the confidentiality capacity (CC). In the proposed scheme, we first establish the OAM channel model under uniform circular array (UCA) misalignment in the line-of-sight (LoS) channel. Facing the risk of information leakage during key negotiation, we couple key generation with the OFDM communication process. Then, we analyze the CC of the OFDM-OAM system and derive its closed-form expression. Simulation results illustrate that the proposed OFDM-OAM PKG scheme has achieved high CC compared with existing works. In addition, as the offset angle of the eavesdropper’s UCA increases, the CC increases, and the bit error rate (BER) of the eavesdropper tends to be 0.5.

Ensuring reliable and secure emergency communications in post-disaster scenarios is challenging, particularly when mobile communication infrastructures are damaged. In response to challenges in post-disaster emergency communications (PDEComs), this article proposes a cooperative relaying system (CRS) enhanced with Internet of Things (IoT) technology. The system features an airship equipped with buffers that serves as an aerial relay, designed to improve data transmission performance and communication security. To achieve this, it incorporates physical layer security techniques. Additionally, we introduce a hybrid mechanism that combines nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA), facilitating flexible resource allocation in IoT-enhanced CRSs. To fully leverage the advantages of the proposed airship-and-buffer aided CRS, we formulate a weighted secure sum rate (WSSR) maximization problem, jointly considering the power control, mode selection, and information security. Initially, we address the formulated WSSR maximization problem using Lyapunov optimization. Subsequently, the primal problem is divided into four cases, from which optimal power control and mode selection policies can be derived. This process is constrained by the stability of buffer queues and privacy transmission requirements. Finally, the simulation results show that the proposed scheme outperforms state-of-the-art schemes in terms of the WSSR. By adopting the airship and the hybrid NOMA/OMA mechanism, the WSSR can be increased by 29.9% and 96.4%, respectively. Moreover, we explore the impact of network parameters (e.g., the distance between the eavesdropper and airship, and decoding thresholds) on information security.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV’s location, designing the RIS’s phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

In this paper, we investigate the secrecy performance of the UAV short-packet communication system assisted reconfigurable intelligent surface (RIS). In this system, based on the phase shift differences of the RIS, the Gamma and exponential distributions are used to match the received signal-to-noise ratio (SNR) at the link terminals. Closed-form expressions for both the probability density function (PDF) and the cumulative distribution function (CDF) are derived. Based on the above PDF and CDF, we derive closed-form expressions for the average achievable rate (ASR) and the average secure block-error rate (SBLER) to evaluate the system’s security and reliability performance. In addition, a novel analytical model is proposed, which can simplify the calculation of the secrecy outage probability (SOP). Furthermore, to explore the performance boundaries, we also derive closed-form expressions for the asymptotic SOP and the asymptotic probability of positive secrecy capacity (PPSC) in high-SNR regions. The accuracy of the derived expressions is validated through simulations and numerical results, which also demonstrate the effectiveness of the proposed SOP analysis framework. In the simulation, we investigate the impact of key system parameters, such as the number of RIS elements, finite block-length channels, UAV altitude, Rician factor, as well as reliability and confidentiality constraints, on the overall system performance. The simulation results show that the proposed system provides significant performance advantages in ensuring secure and reliable transmission under various operating conditions.

This paper investigates the integration of the non-orthogonal multiple access (NOMA) technique and autonomous aerial vehicles (AAVs) in Internet of Vehicles (IoV), aiming to provide flexible access and improve communication coverage for vehicle platooning. The goal is to accurately analyze performance and reasonably optimize network design for AAV-assisted vehicle platooning in NOMA-enhanced IoV. To achieve this, an analytical solution is derived for the average achievable rate from the lead vehicle to follower vehicles over Rician fading channels. Leveraging this analytical solution, the Gauss-Chebyshev integration is employed to obtain the approximate solution. Then, we formulate a problem of maximizing the sum of secure rates by optimizing the trajectory and spectrum allocation. The formulated problem is constrained by the security requirement and imperfect channel state information. Addressing the NP-hard nature of this problem, an iterative optimization algorithm is developed, incorporating Q-learning and the graph theory to alternately adjust the trajectory and spectrum allocation. Finally, the simulation results show that the approximate solution matches well with the analytical solution, and the gap is less than 6%. Moreover, the proposed scheme has a significant performance improvement in the sum of secure rates compared with the state-of-the-art schemes.

Incorporating an active reconfigurable intelligent surface on an autonomous aerial vehicles (AAVs), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in AAVs, energy management emerges as a key challenge within AAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the AAV’s position and designing the active beamforming. For the AAV’s position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the AAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in AAV networks.

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

In the context of rapid urban informatization, numerous vehicular devices have undertaken the responsibilities of local storage and data processing, with Federated Learning (FL) assuming a pivotal role within Vehicular Edge Computing Networks (VECNs). However, disparities in data quality and resources among vehicles may pose challenges to the efficiency of FL. To this end, we investigate the client selection and resource allocation issues specific to Unmanned Aerial Vehicle (UAV)-assisted vehicles within the domain of FL. Firstly, we construct a dynamic interactive reputation model where UAVs evaluate and select client vehicles based on factors like performance and capability, effectively filtering out high-quality data sources and enhancing the system’s ability to resist malicious node attacks. Secondly, we formulate a joint optimization problem to design a scheduling strategy that efficiently manages computational resources and communication capabilities, thus controlling latency and reducing energy consumption resulting from local model training. Additionally, we propose an asynchronous parallel Deep Deterministic Policy Gradient (APDDPG) algorithm with shared experience replay, aimed at enhancing the stability of global model convergence. Simulation results reveal that our proposed model and algorithm can more effectively resist attacks from malicious nodes and more fully utilize resources compared to other approaches, ultimately achieving efficient FL.

As an important part of Internet of Things (IoT), the Internet of Vehicles (IoV) has been widely used in traffic intersection control, automatic driving, intelligent navigation, etc. However, due to the dynamic topology and high mobility, IoV faces the challenge of frequent disconnections, which will lead to deterioration in the performance of data dissemination. Motivated by the above, air-to-ground (A2G) integrated IoV is used to bridge the communication gaps between terrestrial vehicles to achieve efficient information transmissions. This paper investigates the application of low altitude platform stations (LAPSs) and reconfigurable intelligent surface (RIS) in A2G integrated IoV, where multiple relaying LAPSs equipped with RISs are adopted to improve the spatial multiplexing gain and create the smart radio environment. To make full use of the advantages of LAPS-and-RIS enhanced transmissions, we formulate a weighted sum rate (WSR) maximization problem by jointly considering the location, power, and phase shift. To tackle this challenging non-convex problem, we design an iterative optimization scheme, where three optimization variables are processed in turn. Simulation results demonstrate that the proposed WSR maximization scheme can significantly improve the communication performance in comparison with other state-of-the-art schemes and the baseline scheme.

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

This paper proposes an active aerial reconfigurable intelligent surface (ARIS) assisted secure communication framework by integrating sensing and positioning against a mobile eavesdropper. In the proposed scheme, the base station (BS) beamforms the private information to the legitimate user and jams the eavesdropper with artificial noise (AN), while reconfiguring the phases and amplitudes of the passive signal by the active ARIS for promoting secure communications. To acquire the channel state information of the time-vary wiretap channel, the BS tracks the position of the eavesdropper by exploiting the reflected AN. Based on the tracked position of the eavesdropper in the previous time slot, we propose a secure communication scheme that aims to maximize the secrecy rate in the current time slot. This scheme is assisted by the ARIS through jointly optimizing the passive beamforming of the privacy information and AN, the reflection matrix of the ARIS, and the position of the ARIS. In the case of this non-convex quandary with highly coupled variables, we opt to disassemble it into three constituent subproblems and design an alternating optimization framework, where the optimal power beamforming at the BS is derived using a successive convex approximation method and semi-positive definite relaxation technique, the reconfigurable coefficient of the ARIS is optimized using the majorization-minimization algorithm, and the optimal position of the ARIS using the three-dimensional network is obtained by the deep deterministic policy gradient algorithm. Simulation results demonstrate the superior performance of the proposed scheme in the context of the secrecy rate when compared with benchmark schemes. By adopting the active beamforming and positioning technique, the secrecy rate can be increased by 38.3% and 10.8%, respectively.

Unmanned aerial vehicle mounted base stations (UAV-BSs) are expected to become an integral component of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructures. Motivated by the above, this paper proposes an aerial-ground integrated vehicular networking architecture, based on which a UAV-vehicle collaboration perspective is proposed. Specifically, an emerging vehicle-to-UAV (V2U) and vehicle-to-vehicle (V2V) collaboration framework is first presented to facilitate diverse vehicular applications. Next, we investigate the coverage radius maximization problem by optimizing the UAV-BS altitude. Meanwhile, by taking the channel state information (CSI) feedback delay into account, we formulate a V2U communication sum rate maximization problem by optimizing the power control and spectrum allocation, which is constrained by the capacity and reliability requirements. Then, we derive the closed-form expression of optimal UAV-BS altitude. Afterwards, we decouple the formulated sum rate maximization problem, and devise an efficient algorithm with polynomial complexity, where the optimal power control and spectrum sharing are solved. Finally, simulation results demonstrate that the maximum coverage radius and optimal UAV-BS altitude can be achieved by our proposed scheme in different urban environments. In addition, our designed scheme can effectively improve the V2U communication sum rate in comparison with the current works.

In this paper, a novel reconfigurable intelligent surface (RIS)-assisted HAP-UAV secure multi-user mixed radio frequency (RF)/free space optical (FSO) system is proposed. Specifically, the Gamma-Gamma distribution is utilized to characterize the atmospheric turbulence effect for the FSO link from UAV to HAP, while the Rayleigh and Nakagami-$m$distribution fading are applied to simulate the legitimate and wiretap RF links, respectively. We present the closed-form expressions for the probability density functions, the cumulative distribution functions, and the secrecy outage probability (SOP) of the end-to-end signal-to-noise ratio (SNR) in terms of Meijer’s G-function. To gain more insight into secrecy performance, we further obtain the closed-form expressions for the asymptotic SOP, the asymptotic probability of positive secrecy capacity (PPSC), the diversity gain, and the coding gain at high SNR regions. We can observe that the secrecy performance depends on the weaker channel between the RF and FSO, and is closely related to the number of RIS elements, the number of terrestrial users, the atmospheric turbulence factor, pointing error parameters, and the fading parameter of Nakagami-$m$distributed wiretap link. Finally, numerical results validate the derived results and demonstrate that the proposed design achieves superior secrecy performance over the benchmarks.

The proposed study introduces a reconfigurable intelligent surface (RIS)-aided hybrid radio frequency (RF)/free space optical (FSO) system with an unmanned aerial vehicle (UAV) relay to enable an ultra-dense sixth-generation (6G) network. The channels for RF and FSO are represented by Rayleigh and Gamma-Gamma probability distributions, correspondingly. Additionally, the network includes a terrestrial eavesdropper that follows the Nakagami-m distribution, attempting to breach confidential information. To counter this threat, RIS technology is used to enhance the hybrid system's secrecy. The study conducts a closed-form analysis of the secrecy outage probability (SOP) and obtains its asymptotic expression for determining the diversity order and coding gain. Theoretical findings have been confirmed through thorough numerical simulations implemented with the Monte-Carlo approach. The findings demonstrate the RIS technology's effectiveness in enhancing the network's secrecy performance.

With the development of sixth generation (6G) networks, semantic communication is treated as an emerging technology to provide more intelligent communication between two parties for enhance the accuracy and efficiency of communications. Whereas, it demands to merge all physical layer blocks in the conventional communications, which heavily deteriorates the spectrum sparsity problem of wireless communication networks. For this sake, we develop a novel intelligent relay assisted semantic communications, by enhancing communication efficiency, which creates a paradigm for text transmission fusion in 6G networks. In this contribution, the semantic communication system we have designed combines traditional deep learning methods with intelligent semantic relays, based on which, the protocols about minimizing the semantic errors by recovering the meaning of sentences for addressing channel variations issues. Meanwhile, our designed system could be applied in the case of wireless channel deterioration or knowledge background mismatch at the transmitter and receiver, and is also a paradigm for future applications in one-to-many and many-to-many semantic communication. At last, we propose a range of research directions and open challenges for boosting the implementations of relay assisted semantic communications.

In this article, we present a drone-relayed vehicular networking architecture, which aims to improve the achievable data rate of cell-edge vehicles in rural highway scenarios. Specifically, we first incorporate the decode-and-forward (DF) relay protocol with the nonorthogonal multiple access (NOMA) and maximum ratio combining (MRC) techniques, based on which an NOMA- and MRC-Enabled framework is proposed. Next, to fully exploit the advantages of the proposed framework, we separately formulate the total achievable data rate maximization and energy consumption minimization problems by jointly considering the height and 2-D trajectory optimization of relaying drone. The formulated energy consumption minimization problem is transformed into a trajectory optimization problem with obstacle avoidance constraints. Then, for the total achievable data rate maximization problem, we utilize the golden section method to design a height optimization scheme with polynomial complexity. Afterward, we improve the particle swarm optimization (PSO) algorithm, and present an effective 2-D optimization scheme. In addition, the performance superiority of the proposed NOMA- and MRC-Enabled framework is analyzed theoretically. Finally, simulation results verify the efficacy of the proposed height and trajectory optimization schemes. For instance, by using the NOMA and MRC techniques, the total achievable data rate can be improved by 24.4%. Moreover, within the same running time, a shorter trajectory can be obtained by adopting our presented trajectory optimization scheme in comparison with the current works.

Space–air–aqua integrated network (SAAIN) is an emerging maritime network architecture to support reliable and timely communications. Considering the computation capability and information security in maritime transportation systems, this work proposes a double-edge secure offloading scheme, where both base-station (BS) and satellites provide secure mobile-edge computing services for delay-sensitive applications. Specifically, maritime mobile users may offload their computation tasks adaptively to the BS or satellites securely relayed by unmanned aerial vehicles (UAVs). To minimize offloading delay, we formulate an optimization problem to allocate the transmit power cooperatively via UAVs’ trajectory optimization. Moreover, jamming UAVs are deployed to protect the offloading process. For such a nonconvex optimization problem, two iterative algorithms are proposed to determine the transmit power and design the UAVs’ trajectories. Numerical results show the effectiveness of the proposed scheme in terms of offloading delay.

There is an increasing interest in direction-of-arrival (DOA) estimation using nested arrays composed of vector sensors. Considering acoustic vector sensors (AVSs) commonly used in underwater applications, this paper proposes a novel algorithm, called augmented nested quaternion-MUSIC (ANQ-MUSIC), to perform DOA estimation. By judiciously arranging the multi-component outputs of AVSs, we model the received signals from the entire nested AVS array as a quaternion observation vector in a compact way to reduce the computational complexity. Next, we formulate a quaternion-based difference co-array (QDCA) model via vectorizing the quaternion covariance matrix (QCM). Based on the obtained insights from the QDCA model, we derive a suitable QCM, which is constructed by applying the spatial smoothing technique. Finally, classical quaternion-MUSIC is logically introduced to estimate the DOA parameters. In simulations, we take into account non-uniform received noise and inter-component correlated noise, which may occur in practical underwater environments. The results demonstrate that the proposed method shows superiority in angular resolution and achieves a desirable trade-off between estimate accuracy and computational burden, besides showing robust performance in the above test scenarios.

Medical-care sensor networks promote the rapid development of telemedicine applications. However, in poverty-struck, disaster-struck or remote areas with limited infrastructures, it is difficult to provide fast and timely medical-care services. To address this challenge, we propose an aerial base station (ABS)-assisted medical-care sensor network, based on which the data transmission problem is investigated by jointly considering the anti-interference and anti-collision requirements. Specifically, in order to reduce the bit error rate caused by electromagnetic interferences, we first design an anti-interference method based on M-ary spread spectrum and multi-carrier modulation. Then, by introducing a multi-frequency sensor identification mechanism, an anti-collision method based on time division multiple access and frequency division multiple access is presented. Finally, simulation results demonstrate that our proposed scheme has significant advantages in anti-collision and anti-interference compared with current schemes. In quad-interference scenarios, the anti-interference performance is improved by 5.3 dB. Moreover, the anti-collision performance is also increased by 17.2%. Furthermore, in scenarios with a large number of sensors, the successful sensor identification percentage is always greater than 50%.

Radio signals are electromagnetic waves that are propagated in freespace. This nature makes it vulnerable to be attacked from eavesdroppers. Fortunately, with the aid of the reconfigurable intelligent surface (RIS), which passively reflects the incident signal, the spatial distribution of the signal strength can be customized to benefit legitimate users. In this work, we propose a RIS aided non-orthogonal multiple access (NOMA) transmission scheme to provide secure links for two users, where the unmanned aerial vehicle (UAV) equipped with RIS serves as a relay to change radio coverage flexibly. In the proposed scheme, the NOMA transmit power of base-station (BS), the UAV's location, and the RIS phase shift are jointly optimized to maximize the secure transmission rate, which is a nonconvex optimization problem. For this non-convex problem, we first decompose it into three subproblems. Then an efficient iterative algorithm is proposed, where the transmit power and UAV's location are optimized through the successive convex approximation (SEA) method, and the phase shift is optimized through the semi-definite relaxation (SDR) strategy. Numerical results verify the secrecy superiority of the proposed scheme compared with the current schemes.

The promising physical-layer authentication (PLA) scheme enjoys low computational complexity and provides a lightweight solution for the wireless transmission security problem. However, the ideal but impractical assumptions are made on the priori knowledge in the traditional PLA schemes, which fail because the priori knowledge can be interfered by the impersonation attacks. Hence, a blind PLA scheme featured by the composite radio sample characteristics, active monitoring slots, and blind hypotheses tests is proposed in this paper. In particular, the intrinsic location-specific channel response integrated with the transmitter-specific signal knowledge is sampled during the active monitoring slots and is used to conduct minimum error (ME) and Neyman-Pearson (NP) hypothesis tests. The authentication reliability is enhanced significantly by jointly using the ME and NP criteria. For both the stationary and the non-stationary signal cases, our theoretical analysis shows that the temporal channel variations, the spatial channel correlations and the spectral bandwidth contribute to improve the authentication reliability. Our simulation results not only demonstrate the effectiveness of the proposed PLA scheme, but also indicate the possible scenarios in which ME outperforms NP, and NP outperforms ME.

Medical-care Internet of Things enables rapid medical assistance by providing comprehensive and clear healthy information. However, due to the limited infrastructure, it is difficult to quickly and securely transmit medical-care information in poverty-stricken or disaster-stricken areas. To tackle the above situation, in this article, we propose a delay-sensitive secure nonorthogonal multiple access (NOMA) transmission scheme with the high-altitude platform (HAP) and low-altitude platforms (LAPs) cooperated to securely provide delay-sensitive medical-care services. In the proposed scheme, we first design a novel HAP–LAP secure transmission framework to provide NOMA communication services to multiple hotspots. Constrained by the limited power and spectrum, we formulate an optimization problem, such that the privacy information delay is minimized. For thisnonconvex optimization problem, we design an alternating optimization framework, where the power, spectrum, and LAPs’ location are tackled in turn. In addition, we theoretically analyze the performance superiority compared with the orthogonal multiple access scheme and derive the secrecy outage probability closed-form expression. Finally, numerical results show the performance superiority of the proposed scheme compared with the current works with respect to the secure information delay.

Internet of Things (IoT) can be conveniently de-ployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the IoT data collection with blockchain-based security provisioning, towards efficient and safeguarded IoT operations. In particular, a blockchain with proof-of-stake (PoS) consensus mechanism is constructed among the UAVs with the collected IoT data. Correspondingly, we optimize the IoT communication and the UAV deployment for the maximum blockchain throughput considering the PoS procedure. The problem is solved with a deep deterministic policy gradient-based approach, where the power allocation is obtained with closed-form solutions and the UAV deployment is learned with actor-critic networks. Simulation results are provided to show the deployment and performance, corroborating the effectiveness of our proposal.

Federated learning (FL) has been proposed to coordinate multiple edge user equipments (UEs) for training a global model. However, the FL's performance is affected by the channel state of wireless network. Specifically, the performance of the random selecting UE is seriously limited by the millimeter-wave (mmWave) channel. In this paper, we propose to deploy intelligent reflecting surface (IRS) to reconstruct the non-line-of-sight (NLoS) mmWave channel. A novel UE scheduling strategy is then proposed to optimize the FL system performance. For selecting the particular UEs and achieving higher convergence, we formulate an optimization problem that jointly optimizes the aggregation vector of the base station (BS) and the IRS phase shift matrix. To figure out the formulated problem, we propose a two-step difference-of-convex (DC) algorithm. Simulation results demonstrate that the proposed algorithm can achieve higher convergence and a lower training loss than the benchmark algorithm.

The unbalanced traffic distribution is a severe problem in cellular networks, which leads to congestion and reduces spectrum efficiency. To tackle this problem, we propose an unmanned aerial vehicle (UAV)-assisted wireless network architecture in which UAV acts as relay to divert the traffic from the overloaded cell to its neighbor underloaded cell. Considering that UAV communications are easily eavesdropped, we use the secrecy capacity to evaluate the performance of the network. To fully exploit the advantages of the proposed architecture, we formulate a joint UAV position optimization, user association, and time allocation problem to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position, and then use the branch-and-bound method to devise a low-complexity algorithm to get the optimal user association and time allocation schemes. The simulation results indicate that the proposed UAV-assisted wireless network architecture is superior to the terrestrial network, and the proposed algorithms can further improve the network performance in comparison with the other schemes.

In this paper, we investigate the resource allocation and vehicle to everything (V2X) offloading in the cognitive vehicular networks. The cognitive radio (CR), mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) schemes are applied aim to solve the combinational problem of resource allocation and V2X offloading. The problem for jointly optimizing power and time allocation in the MEC based CR (CR-MEC) networks is conceived. We decompose the joint optimization problem into two subproblems, which are power allocation and time allocation problems. In order to solve this joint optimization problem, an advanced comprehensive resource allocation (ACRA) algorithm based on three-sided matching theory is employed. More specifically, the proposed algorithm is to realize the most reasonable matching among primary users (PUs), cognitive users (CUs) as well as a cognitive base station (BS), and put forward a V2X offloading strategy, by appropriately allocating power and time aim to minimize the system energy consumption. The simulation results show that, our proposed algorithm converges to stable. Furthermore, the proposed NOMA based CR-MEC networks can achieve lower energy consumption compared to the orthogonal multiple access (OMA) based CR-MEC networks.

Buffer-aided relay technique can improve the diversity order and offer secrecy provision. To further improve secrecy performance, this paper proposes a secure link selection for relay networks where a new link selection policy is first designed under the constraint on the buffers and channel states using a Markov chain. The stationary state and the corresponding state transition matrix can be derived, and they are used to analyze the secrecy performance. Through the derivation of secrecy outage probability, we can get its closed-form expressions. Numerical results demonstrate that the proposed secure transmission scheme has a better performance than the conventional buffer-aided secure transmission schemes in terms of secrecy outage probability.

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

As industrial production gradually becomes more intelligent, 5G technology has great application prospects in industrial environments. However, the channel in the industrial environment is different from the channel in the other scenarios, and hence need to be characterized specifically. In this paper, a measurement campaign and modeling of industrial scenarios are presented. And we study the main three frequency bands of 3.5GHz, 4.9GHz and 5.8GHz in Sub-6G. Then we model the path loss to fill the gaps in measurement modeling currently in large indoor industrial scenarios. At the same time, we also modeled and analyzed the shadow fading. The results show that the shadow fading in the industrial environment conforms to the lognormal distribution.

Cross-scene classification of hyperspectral image (HSI) has been increasingly researched due to its crucial utilization in practical applications. However, cross-scene data generally perform distribution discrepancy, which hampers the transfer learning performance. To address this issue, deep conditional distribution adaptation networks (DCDAN) are proposed for HSI cross-scene classification, which aim to reduce the distribution shift between a source domain and a target domain. The proposed deep network adopts a conditional constraint to match the class conditional distributions across domains, where a great number of training samples from the source domain and a small number of training samples from the target domain are utilized to train the deep model. Cross-scene classification results on two HSIs demonstrate that the proposed network is able to yield superior performance compared with some related methods.

Non-orthogonal multiple access (NOMA) and device-to-device (D2D) are two key technologies of the fifth-generation wireless networks. In this paper, we propose a new D2D-and-NOMA integrated framework, where the D2D users (DUEs) can reuse the spectrum of the cellular users (CUEs) in four NOMA-aided spectrum-sharing modes. In order to fully exploit the potential of the proposed framework, we jointly optimize user pairing and power control to maximize the number of accessed D2D links and meanwhile reduce the total power consumption under the constraints of the decoding thresholds of the DUEs and CUEs. We first analytically obtain the optimal transmission power for each DUE-CUE pair. Then, based on the power control policy, we reformulate the user pairing problem as a min-cost max-flow problem in graph theory and solve it efficiently. Specifically, our proposed algorithm can solve the formulated problem optimally with low complexity. Finally, simulation results indicate that our algorithm can significantly improve the number of accessed D2D links and reduce the power consumption in comparison with the other schemes.

In this paper, by using a dual-band channel sounder equipped with omnidirectional antennas, we have measured and investigated the propagation parameters including path loss and shadowing at 1.79 and 4.9 GHz in the typical non-line-of-sight (NLOS) urban macrocell (UMa) scenario. Two different large-scale channel models, the alpha-beta-gamma (ABG) model and the close-in model with a frequency-weighted path loss exponent (CIF), are derived according to the channel measurement data. Finally, we compare the parameter values, shadow fading (SF) standard deviations, and prediction errors of the two models, and evaluate the accuracy and sensitivity with respect to the measurement data. The numerical results show the improvement in the channel fading estimation of the CIF model compared with the ABG model.

In order to protect the primary information and provide some licensed spectrum for the secondary system, we propose a secure transmission scheme, where the primary privacy information is protected by a full-duplex secondary system. In the proposed scheme, the secondary receiver utilizes one antenna to receive the secondary information, and the remaining antennas will be utilized to jamming the eavesdropper. In addition, the secondary system allocates a fraction of the transmit power for the secondary transmission and the remaining power will be allocated for jamming protection in the null space of both the primary and secondary transmission links. For the proposed scheme, we first analyze the primary secrecy outage probability and the secondary transmission rare as well as the energy-efficiency. Moreover, we optimally allocate the secondary transmit power such that the primary average secrecy rate is maximized under the requirements of the secondary transmission rate and energy-efficiency. Numerical results have been given to show the performance improvement of the proposed scheme about both the primary and secondary performances.

In order to secure the primary privacy information and provide quality-of-service provisioning for the secondary system, we propose a secondary encryption secure transmission scheme. In the proposed scheme, the primary system utilizes the secure secondary messages to encrypt the primary confidential messages and the secondary system can acquire some spectrum opportunities. Specifically, when the primary system is secure, the primary information can be directly transmitted; when the primary system is insecure while the secondary messages can be securely transmitted, the primary system utilizes the secure secondary messages to encrypt the primary information; otherwise, the spectrum will be utilized for secondary transmission. For the proposed scheme, we investigate the performances of the primary ergodic secrecy rate and the average secondary throughput. Numerical results have demonstrated that the secondary encryption secure transmission scheme can secure the primary privacy messages and improve the secondary transmission throughput.

Physical layer security is a promising technology for secure communication by using wiretap encoding or physical-layer secret key generation. Most of the existing research works study these two techniques separately, which however cannot fully utilize the radio resource. In this paper, we combine the two techniques by using a channel-aware threshold-based approach. Specifically, when the estimated channel gain is larger than the threshold, the wiretap encoding is adopted; otherwise, the secret key generation is performed. For given constraints on security and reliability, we respectively derive the effective secrecy rate of the two techniques and then obtain the secrecy throughput of the proposed scheme through simulations. Numerical results show that the proposed scheme can effectively combine the two techniques and enhance the average secrecy throughput.

We propose two cooperative secure transmission schemes to protect a two-hop buffer-aided network assisted by an energy harvesting relay. In the first scheme, we assume that the knowledge of the energy harvesting and fading channels states is known in a non-causal manner (offline). In the second scheme, we assume that this knowledge is known in a causal manner (online). For both schemes, we first design an effective link selection policy by taking into account of the transmission efficiency and information security requirements. We then optimally allocate the harvested power at the relay node. For the offline scheme, we maximize the average secrecy rate under the stability constraints of the data queue and the energy queue according to the proposed link selection policy, and design a two-stage iterative algorithm to select the transmission link and allocate relay’s transmit power. In the online scheme, we first model the average secrecy rate maximum problem as a Markov decision process, and then utilize the causal knowledge to select the best transmission link and optimally allocate relay’s transmit power. In addition, the exact and asymptotic closed-form expressions are derived for the ergodic secrecy rate. Numerical results are presented to validate our analysis and demonstrate that the proposed schemes outperform the other buffered-aided secure transmission schemes assisted by the energy harvesting relay in terms of average secrecy rate.

In this paper, we propose a cooperative secure transmission for two-hop relay network with limited feedback. In the proposed scheme, the channel state information associated with cooperative users is quantized and limited bits are feedback for cooperative relay and jammer selection. By considering the quantization error brought by limited feedback, we investigate the performances of transmission outage probability and secrecy outage probability, and their closed-form expressions are derived. On this basis, we optimal design the target transmission rate and secrecy rate so that the average secrecy rate is maximized under the constraints of maximum permitted transmission outage probability and secrecy outage probability requirements. Simulation results are presented to verify the analytical results for the proposed scheme and prove its secrecy performance improvement in terms of the secrecy performance with light overhead.

Due to the dynamic arrival of data packets and time-varying channel states, secure transmission opportunities will be wasted when there is no confidential message to transmit, and data packet transmission can be delayed while waiting for the next available secure transmission opportunity. In order to seize every precious secure transmission opportunity and reduce the data packet waiting time, we propose a secure transmission protocol in which the under-utilized secure transmission opportunities can be exploited to transmit key packets, and these key packets will encrypt the confidential messages in the subsequent transmissions. Following the above-mentioned principle, we first apply this protocol to a single-input single-output network, and optimally allocate the secure transmission opportunities for the key and data transmissions, such that the secrecy rate is maximized under the constraints of the minimum energy efficiency and queue stability requirements. In addition, the packet delay for the proposed protocol is investigated using a generating function approach and a closed-form expression of the packet delay is derived. Then, we extend our work to the multiple-input single-output network and utilize the key queue as well as the artificial noise to protect the confidential messages. Similarly, we also optimally allocate the transmission opportunities for the key and data transmissions to maximize the secrecy rate and study the data packet delay performance. Since all secrecy transmission opportunities are utilized in the proposed protocol, the full secrecy rate is achieved. Numerical results are demonstrated to verify the performance superiority of the proposed protocols when compared with the other key encrypted schemes in terms of the data packet delay and the secrecy rate.

In this paper, we investigate a new cooperative paradigm to provide information security for the primary system in cognitive two-way networks where the two-way secondary system can access the licensed spectrum to support the secondary quality of service (QoS) requirement as long as the secondary system provisions secure cooperation for the primary system against the malicious eavesdropper. To do so, the secondary system adopts the physical-layer method of cooperative jamming and relaying to protect the primary confidential message in two stages and acquire some spectrum opportunities for the two-way transmission in both stages. In addition, we try to allocate the power for transmitting jamming signal, secondary messages, and relaying messages in such a way that the secrecy capacity of the primary system is maximized subject to the minimum secondary transmission rate requirements. Furthermore, a sequential parametric convex approximation (SPCA) based iterative algorithm is proposed to solve this non-convex problem. Our proposed cooperative transmission scheme is reciprocally- benefited for both systems as the secondary system can access the licensed spectrum in both two slots and the primary confidential message can be protected from eavesdropping. In addition, we analyze the secrecy capacities for asymptotic scenarios. Simulation results demonstrate the performance superiority of our proposed scheme over conventional cooperative secure communication scheme in terms of the primary secrecy capacity.

Aiming at providing secure provisioning for the primary system, in this paper, we propose an energy harvesting based cooperative communication (EHCC) scheme which will protect the primary confidential message from eavesdropping under the constraint of the secondary quality-of-service (QoS) requirement. To do so, the secondary receiver (SR) firstly transmits jamming signal to protect the primary transmission and then, the secondary transmit (ST) harvests part of the received signals and forwards the remaining signals to the primary receiver (PR) concurrently with the secondary transmission. In our proposed scheme, radio- frequency energy harvesting will improve ST's maximum transmit power and the jamming interference at SR can be directly cancelled as SR has transmitted it. Then, we try to allocate the transmit power and design energy harvesting parameters in such a way that the secrecy capacity of the primary system is maximized under the constraint of the secondary QoS requirement. In addition, an iterative algorithm is proposed to solve this non-convex problem. Moreover, we also analyze the primary secrecy capacities and allocate the resource for the asymptotic scenarios. Simulation results demonstrate the performance superiority of our proposed scheme over the conventional cooperative secure communication scheme in terms of the primary secrecy capacity.

This paper investigates the problem of secure communications of the two-hop primary system with the cooperative jamming of the untrusted secondary system. The secondary system is untrusted for the primary system and willing to eavesdrop on the primary signal. In addition, the secondary system is also willing to provide friendly jamming to increase the secure rate of the primary system in reward for being allowed to share the licensed spectrum. Specifically, the cooperative communication is implemented into two slots which correspond to the transmission of the first and second hops of the primary system, respectively. In each slot, part of the slot is allocated for the primary information transmission. Simultaneously, a secondary user (SU) is selected to broadcast jamming signal to protect the secure communication of the primary users (PU) against the other untrusted SUs. Then, the remaining time of the slot is allocated for the secondary transmission. To maximize the transmission rate of the secondary system under the constraint of the target secure rate requirement of the primary system, we optimally select two jamming SUs and determine the time parameters in each slot. SUs' average transmit rate and the lower bound on PUs' secure outage probability are derived. Simulation results demonstrate the performance superiority of our developed strategy over conventional secure communication schemes in terms of PUs' secure outage probability and SUs' average transmission rate.

Aiming at reducing the outage probability of primary users (PU) and obtaining spectrum resources for secondary users (SU), in this paper, we proposed a joint subcarrier and power allocation scheme (JSPA) for reciprocally-benefited spectrum sharing with SUs cooperating with PUs. In JSPA scheme, SUs adopt the decode-and-forward (DF) relaying protocol to help PUs in a two-stage way. Meanwhile, a fraction of unallocated licensed spectrum is allocated for the secondary transmission in every stage. However, if the two-stage cooperation still cannot satisfy PUs' outage quality-of-service (QoS) requirement, SUs then switch to the access mode to entirely capture the licensed spectrum. Our proposal is to maximize the average transmission rate of SUs through joint allocation of subcarriers and power. The closed-form expressions about the outage probability and average transmission rate of both PUs and SUs are derived. Simulation results show that compared with the conventional cognitive cooperation schemes, the average transmission rate of SUs improves.