In overflying Low Earth Orbit (LEO) satellite beam hopping (BH), the rapid variation in topology, intermittent visibility, dynamic traffic evolution, and the large joint beam and power action space collectively lead to significant challenges in beam scheduling. Traditional deep reinforcement learning (DRL)-based methods require exhaustive exploration of beam positions and power combinations, resulting in excessive training time and high computational complexity, which limits their practical deployment during LEO satellite overflight. Future LEO satellite networks demand high throughput to meet the needs of various applications, but the intelligent beam scheduling schemes for overflying satellites face high training complexity. These challenges become more critical for satellite-assisted IoT services, where massive low-rate terminals require wide-area and efficient access. To address this issue, this paper investigates the BH problem for overflying LEO satellite clusters and proposes a complexity-efficient DRL framework. To mitigate the excessive action space and the difficulty of acquiring channel state information during satellite overflight, a statistical pre-association mechanism is first introduced to eliminate infeasible satellite-cell pairs based on long-term traffic and visibility information. On this structured decision space, a constrained scheduler based on Multi-Agent Proximal Policy Optimization performs coordinated beam selection. Furthermore, fractional programming (FP) based power control is embedded into the learning loop to generate high-reward guidance signals, improving policy learning efficiency. Simulation results demonstrate that the proposed framework outperforms conventional DRL-based methods in terms of throughput and delay. Compared to traditional DRL-based methods, the average throughput is increased by 5-30%, and the training time is reduced by 50-60%.

In recent years, orthogonal time frequency space (OTFS) technique has garnered substantial academic attention as a promising solution for ensuring robust and reliable communication in high-mobility wireless communication environments. In this paper, we present a complex-valued graph neural network (CV-GNN) aided signal detection scheme for OTFS modulation, which can mitigate the channel spreading caused by fractional Doppler shifts. To mitigate inter-carrier interference (ICI) and inter-symbol interference (ISI) induced by fractional Doppler shifts and imperfect channel state information, the proposed detector is able to process the received OTFS signal in the complex plane to acquire the complete phase information of effective channel. Simulation results demonstrate that the proposed method can outperform other state-of-the-art schemes by 1∼4 dB in terms of reliability performance.

Integrated air-ground communication (IAGC) has emerged as a promising solution to deliver seamless wireless coverage and high-data-rate services. However, potential malicious eavesdroppers pose a serious threat to the confidential transmission in IAGC due to their non-cooperative behaviors and the inherent openness of communication channels. To tackle this problem, a dynamic spectrum control (DSC)-based transmission scheme is proposed to enhance covert performance and communication reliability in IAGC. With the proposed scheme, we apply the principles of block cryptography, perform adaptive iterative and orthogonal transformations to generate sequence sets that drive transmission decisions. Guided by these sequences, multiple legitimate users can dynamically occupy different frequency slots and transmit data simultaneously. In addition, we analyze the probability of frequency slot multiplexing when several data groups occupy the same frequency slot in a time slot, resulting in the closed-form expression for the detection error probability. We then derive the maximum reliable transmission probability and ergodic rate subject to the covert communication constraints. Simulation results demonstrate that the proposed scheme can achieve superior covert performance compared with benchmark schemes. Furthermore, we evaluate and discuss the effects of key parameters in the proposed DSC-based transmission scheme on communication security and reliability.

In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-k2, for accurate and efficient multipath-aware radio map (RM) construction. As future wireless communication evolves towards environment-aware paradigms, the accurate construction of RMs becomes crucial yet highly challenging. Conventional electromagnetic (EM)-based methods, such as full-wave solvers and ray-tracing approaches, exhibit substantial computational overhead and limited adaptability to dynamic scenarios. Although existing neural network (NN) approaches have efficient inferencing speed, they lack sufficient consideration of the underlying physics of EM wave propagation, limiting their effectiveness in accurately modeling critical EM singularities induced by complex multipath environments. To address these fundamental limitations, we propose a novel physics-inspired RM construction method guided explicitly by the Helmholtz equation, which inherently governs EM wave propagation. Specifically, based on the analysis of partial differential equations (PDEs), we theoretically establish a direct correspondence between EM singularities, which correspond to the critical spatial features influencing wireless propagation, and regions defined by negative wave numbers in the Helmholtz equation. We then design an innovative dual diffusion model (DM)-based large artificial intelligence framework comprising one DM dedicated to accurately inferring EM singularities and another DM responsible for reconstructing the complete RM using these singularities along with environmental contextual information. Experimental results demonstrate that the proposed RadioDiff-k2framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks, while maintaining inference latency within a few hundred milliseconds. Code is available at https://github.com/UNIC-Lab/RadioDiff-k.

The open and vulnerable nature of wireless channels exacerbates security risks in integrated sensing and communication (ISAC) systems, especially when the sensing targets act as potential eavesdroppers (Eves), and these risks intensify with collusion among Eves. To address this challenge, this paper investigates a novel strategy for a robust reconfigurable intelligent surfaces (RIS)-assisted secure ISAC system, where an ISAC base station facilitates simultaneous secure communication with legitimate users and sensing of multiple targets that may serve as Eves. We examine two different interaction mechanisms among Eves, namely, non-colluding Eves (NCE) and colluding Eves (CE), under both perfect and imperfect channel state information (CSI) assumptions. For both mechanisms, we formulate the optimization problem of maximizing users’ sum secrecy rate by jointly designing the transmit beamforming and RIS phase-shifts. This optimization is subject to constraints on transmit power, sensing requirements, and unit-modulus RIS phase shifts. The resulting non-convex problems are solved via alternating optimization (AO) algorithms. Specifically, in order to handle the severely non-convex and coupled objective function and multi-link accumulated channel error constraints caused by CE as well as imperfect CSI, we employ the majorizationminimization algorithm and the S-procedure to convert these problems into tractable forms. Simulation results validate the effectiveness of our proposed algorithms. We highlight that, at the expense of a 15% reduction in the users’ sum rate, our proposed algorithm achieves up to a 185% increase in the sum secrecy rate. Furthermore, we quantify the sensing-security trade-off by analyzing the reduction of the sum secrecy rate induced by sensing requirements, and we reveal the impacts of various factors on the sum secrecy rate, such as RIS element number, channel estimation errors, and sensing thresholds.

The proliferation of unauthorized Unmanned Aerial Vehicles (UAVs) poses significant security risks, necessitating robust detection systems. However, in practical long-range surveillance scenarios, UAV signals often deteriorate due to severe attenuation and complex environmental interference, rendering traditional detection methods ineffective. To address this, this letter proposes a novel signal enhancement framework comprising an improved adaptive Wiener filter. We introduce a dynamic noise spectrum estimation strategy coupled with a decision-directed phase compensation mechanism. This approach effectively suppresses non-stationary background noise while reconstructing high-fidelity time-frequency features by rectifying phase distortions. Experimental results on a proprietary real-world dataset demonstrate that the proposed method significantly improves signal quality, enabling high-accuracy detection using YOLO models even in ultra-low Signal-to-Noise Ratio (SNR) regimes compared to raw data-based baselines.

Unmanned aerial vehicles (UAVs) play a crucial role in modern communication systems owing to their high mobility and broad coverage. However, due to the inherent open nature of the wireless channels, UAV-to-ground links are facing significant security threats from eavesdroppers and malicious jammers. To address these challenges, we propose a dynamic spectrum control (DSC) scheme integrating joint UAV trajectory and transmit power optimization to enhance UAV communication security in this paper. This scheme divides transmission channels from time and frequency dimensions and intelligently generates secure decision sequences using cryptographic principles based on real-time channel states, enabling transmissions for legitimate users without intra-cell interference. Based on a rapid-flooding time synchronization protocol, we analyze inter-cell collision probability (CP) and formulate an optimization problem for the secrecy rate. To further enhance security, we conduct a joint UAV trajectory and transmit power optimization. Through the successive convex approximation (SCA) method, we transform the non-convex optimization problem into a tractable convex form, obtaining a suboptimal solution. Simulations demonstrate that our proposed scheme significantly enhances security compared to conventional UAV communication methods.

In this paper, we utilize frequency hopping spread spectrum (FHSS) to help enhance covert performance in frequency diverse array (FDA) covert system. Compared to conventional FDA covert communication, FHSS signals can hide in different channels by rapidly switching the transmission frequency to avoid being detected. Therefore, FHSS-aided FDA covert system can effectively counter the warden with strong detection ability and enhance covert performance. We focus on maximizing the covert capacity during the entire transmission by jointly optimizing the beamfoming vector and antenna frequency vector at the transmitter. Based on the system equipment constraints, two different optimization schemes, i.e., instantaneous optimization scheme (IOS) and robust optimization scheme (ROS), are proposed to solve the problem. Moreover, to evaluate the covert performance provided by FHSS-aided FDA covert communication, we introduce three benchmark schemes, i.e., phased array (PA) and logarithmic distribution FDA (Log-FDA) and non-FHSS. Simulation results demonstrate that FHSS combined with IOS can significantly improve covert performance compared to other benchmark schemes. In contrast, FHSS combined with ROS can effectively enhance covert performance when transmitting signals over a wide bandwidth.

The beam focusing characteristic of frequency diverse array (FDA) in direction-distance dimension has been widely applied in covert communication. To reduce the covert performance loss caused by time-varying FDA channels, the instantaneous optimization of antenna beamforming vector (ABV) and antenna frequency vector (AFV) is required for FDA. However, due to the difficulty in acquiring instantaneous channel state information (CSI) and the limitations of transmit device, instantaneous optimization scheme (IOS) may not be feasible in practical communication scenarios. Moreover, the existing robust schemes only adopt some simple and fixed AFVs without optimizing AFV. This paper investigates robust optimization scheme (ROS) in FDA-aided covert system, where the optimization frequency of the ABV and AFV at the transmitter can be adaptively adjusted based on the acquired CSIs. We maximize the average covert rate during the transmission by jointly optimizing ABV and AFV, and modify the conventional block successive upper-bound minimization (BSUM) method to solve the optimization problem. Furthermore, both perfect CSI and partial CSI at the warden are considered. Numerical results demonstrate that our proposed ROS outperforms the existing robust schemes, especially in the cases of highly correlated channels and partial CSI. Although ROS is indeed inferior to IOS, enhancing the channel decoupling capability of FDA and deteriorating the detection capability of the warden can effectively narrow the covert performance gap between ROS and IOS. Moreover, the beampattern generated by the optimal ABV and AFV at each time slot ensures that the beamfoming gain is maximized at the legitimate receiver and minimized at the warden simultaneously.

The security and privacy of wireless communications have always been top priorities. In this paper, a new frequency hopping (FH) scheme is proposed for wireless covert communication systems, which can simultaneously achieve high reliability, covertness, and anti-jamming capability. In particular, a high level of covertness is attained by exploiting the noise uncertainty present in the environment. Furthermore, channel equivocation is introduced to ensure the anti-interception and anti-jamming capabilities of FH systems. Specifically, we formulate a joint optimization problem of transmit power and frequency allocation to maximize the warden’s detection error probability, subject to transmission reliability and channel equivocation constraints. The formulated problem is then solved using an alternating optimization algorithm. Subsequently, the final FH sequence is generated by mapping the obtained probability vector to a random sequence. Numerical results demonstrate that our proposed covert FH scheme can achieve superior comprehensive performance (i.e., high reliability, covertness, anti-jamming capability, and sequence unpredictability) compared with other typical schemes.

The Low-Altitude Wireless Networks (LAWNs) has emerged as a cornerstone of next-generation mobile due to their flexibility and adaptability in providing on-demand connectivity. However, ensuring reliable and high-throughput aerial drone communication remains a major challenge, mainly due to the dynamic mobility of aerial drones and the complexity of the wireless propagation environment. Traditional LAWNs rely heavily on channel sampling and real-time feedback, which introduce latency and communication overhead. In this work, we proposeRadioRS, a novel sampling-free aerial drone communication framework that combines Radio Map (RM) prediction with Rate-Splitting Multiple Access (RSMA) to enable robust and efficient communication without requiring explicit channel estimation during flight. RadioRS leverages a RM that provides location-aware predictions of channel state. To enhance the accuracy and generalization of these predictions under complex propagation conditions, we develop a generative model based on the Mamba architecture, which efficiently captures fine-grained correlations in the radio environment. Building on the RM, RSMA is employed to flexibly manage interference and improve spectral efficiency. In addition, we design a Mamba-powered controller that adapts beamforming strategies from the RM directly, further improving link reliability and throughput. Comprehensive simulation results demonstrate that the proposed RadioRS framework significantly outperforms conventional channel-sampling-based approaches in terms of both communication reliability and throughput.

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments show that this integrated GenAI framework consistently surpasses traditional interpolation and deep learning methods, especially under sparse sensing conditions. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental building outlines perception, just relying on pathloss, through integrated sensing and communication (ISAC). The proposed method operates on routine communication measurements, without new waveforms, specialized feedback, or protocol changes, thereby enabling a plug-and-play ISAC capability. Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using a generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

We propose an innovative physical layer authentication method, leveraging deep learning to robustly safeguard millimeter wave communications against pilot contamination and clone attacks. Unlike traditional upper-layer authentication mechanisms, our method capitalizes on the spatial-temporal characteristics of millimeter wave channels to extract unique fingerprints, thus establishing a lightweight channel-based authentication technique. Existing methods largely overlook pilot contamination attacks, which may severely degrade the performance of physical layer authentication. Furthermore, traditional threshold-based methods struggle to differentiate between multiple nodes, while supervised learning-based methods are practically constrained due to the unavailability of attackers’ instantaneous channel state information. Moreover, traditional real-valued deep neural networks are inefficient in utilizing the phase information of complex-valued channels, rendering them inadequate for designing practical physical layer authentication schemes. To address these challenges, we propose an autoencoder, empowered by an alternating direction method of multipliers, which can detect and mitigate pilot contamination attacks by exploiting the inherent sparsity of channels. Subsequently, we design a weighted loss function to optimize the proposed classifiable autoencoder to strike an effective balance between detecting clone attacks and authenticating multiple nodes. Finally, to further enhance feature extraction from complex-valued channels, we customize a complex-valued classifiable autoencoder incorporating an innovative complex-valued long short-term memory module. Our simulation results unveil that the proposed method significantly outperforms existing approaches in maintaining high authentication accuracy even under pilot contamination, achieving a desirable trade-off between false alarm and detection rates. Additionally, our proposed complex-valued neural networks further enhance the accuracy of clone attack detection and multiple legitimate nodes authentication.

The rise of heterogeneous aerial and space platforms within Space-Air-Ground Integrated Networks (SAGINs) introduces significant challenges, as the limited spectrum resources force these platforms to operate within shared frequency bands, resulting in co-existing systems. Effective interference management in such networks requires both the design of communication channels and the dynamic mitigation of interference between them. Prior research has largely focused on interference mitigation with fixed communication links, often overlooking adaptive channel selection, which can result in performance degradation. In this study, we address this limitation by introducing MetaRS, an innovative, self-intelligent rate-splitting solution designed for more flexible interference management in co-existing SAGINs. MetaRS enables adaptive channel and communication scheme selection, by leveraging a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA)-based framework enhanced with a one-pass diffusion model. Specifically, the FD-RSMA-based framework allows MetaRS to dynamically shift its interference management strategy according to the current network status. The integration of the diffusion model further enhances MetaRS by allowing it to recognize and adapt to real-time channel conditions and user deployment, thereby enabling self-intelligent interference mitigation. Simulation results demonstrate that MetaRS significantly outperforms conventional SDMA, RSMA, and FD-RSMA approaches. This improvement stems from MetaRS’s joint optimization of channel selection and its adaptive, intelligent interference management capabilities, which effectively balance channel utilization and mitigate interference in complex, multi-platform environments.

The challenge of mitigating interference in Space-Air-Ground Integrated Networks (SAGINs) is exacerbated by the inherent channel uncertainty, which arises due to dynamic weather conditions, heterogeneous user deployment, and different altitude of transmitters. To tackle this problem, Rate-Splitting Multiple Access (RSMA) has been seen as a promising solution due to its robustness. However, conventional beamforming designs for RSMA often suffer from two major limitations: high processing delays and overfitting to specific channel conditions. When the channel conditions change, the performance of these predictors degrades significantly, limiting their effectiveness in dynamic environments. To address these challenges, we propose a novel Fast-Adaptive Predictive Beamforming (FA-PB) framework for RSMA in SAGINs. Unlike traditional predictive beamforming approaches that rely on fixed predictive models, FA-PB integrates a transfer-learning-based online learning mechanism. This innovative approach allows the predictor to dynamically adapt to new channel conditions with minimal computational overhead. FA-PB achieves this by leveraging few-shot Channel State Information at the Transmitter (CSIT) samples, enabling real-time updates and adjustments to the predictor. Consequently, FA-PB ensures that the beamforming process can rapidly adapt to fluctuating channel conditions, maintaining high levels of performance even in highly dynamic SAGIN environments. Extensive simulation results validate the superiority of the FA-PB framework, demonstrating its enhanced adaptability and improved beamforming performance in SAGINs.

Satellite communication has shown great potential for providing ubiquitous connectivity and broadband mobile communications, owing to its advantages of wide coverage, large system capacity, and high transmission rate. However, the increasing complexity of the electromagnetic scenarios poses an unprecedented threat to the security and reliability of satellite covert communication. In this paper, a dynamic spectrum control (DSC) transmission scheme for enhancing the covertness of satellite communications is proposed. The scheme generates a sequence family based on chaotic mapping, controls data decisions and switching processing. With the assistance of this sequence family, the authorized satellite user can unpredictably and dynamically occupy frequency slots for transmitting information. Then, the closed-form expressions of Bit Error Rate (BER) and detection probability is derived. Numerical results demonstrate that the proposed scheme outperforms the benchmark scheme in terms of the overall system performance. Besides, the influences of key parameters in the proposed scheme on the covertness of the system are further analyzed.

Unmanned aerial vehicle (UAV) data collection problems in wireless sensor networks (WSNs) under random and uncertain environments are critical challenging, due to massive burden of real-time communication among UAVs for aligning with observation and state information, ect. For this sake, this work investigates the UAV cooperation problem of WSN data collection by jointly maximizing collected data amount while minimizing cooperation cost. We formulate the problem as a constrained partially observable Markov decision process (CPO-MDP), which stimulates the design of a novel safe reinforcement learning aided lightweight cooperation (SRL-LC) framework for multi-UAV data collection. Speficially, the self-conscious cooperative communication scheme is developed to assist the optimization of the UAV trajectory decision making. Additionally, a safety module embedded in the decision network integrates a relaxed artificial potential field (APF) algorithm, enabling UAVs to maintain safety distance constraints during training. Simulation results demonstrate that the proposed SRL-LC framework achieves data collection performance comparable to the full-cooperation scheme for various settings of prior information, while reducing communication cost by approximately 85%. Moreover, the SRL-LC framework ensures zero violations of safety constraints throughout the training process.

The integration of beam hopping (BH) technology into Low Earth Orbit (LEO) satellite communication systems has emerged as a critical strategy to enhance spectral efficiency and flexibility. However, conventional BH methods often exhibit insufficient robustness when confronted with uneven terrestrial traffic demands and dynamic jamming levels. This paper addresses this challenge by proposing a novel hybrid framework that synergizes statistical planning with multi-agent reinforcement learning (MARL) to achieve anti-jamming beam hopping for LEO satellites. The proposed framework decomposes the BH scheduling process into two phases: statistical planning and real-time adjustment. A portion of the beams are managed using a low-complexity potential game algorithm for statistical planning, ensuring stability and efficient resource allocation based on statistical traffic patterns and jamming conditions. The remaining beams are dynamically adjusted using a low information exchange Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm, enabling rapid adaptation to instantaneous jamming events and fluctuating user demands. This framework reduces the number of beams considered during training, thereby simplifying the action space. As a result, the system achieves quicker convergence and offers enhanced robustness, especially in environments where some information may be missing. Simulation results demonstrate that the proposed method significantly enhances throughput, improves robustness against jamming, and reduces training time compared to other baselines. The integration of statistical planning and mean-field MARL effectively balances long-term efficiency with real-time adaptability, achieving high-quality communication in low Earth orbit satellite coverage areas under high dynamic environments.

In this work, we investigate the Age of Information (AoI) in aerial intelligent reflecting surfaces (IRSs) assisted covert data collection in Internet of Things (IoT) networks. Operating with the autonomous aerial vehicle (AAV) and IRS can improve the data transmission covertness, as well as the data freshness by reconstructing the wireless propagation environment. Specifically, we consider a scenario in which ground IoT sensors transmit confidential information to the legitimate receiver (Bob), through IRS assisted AAV relay, at the same time, an eavesdropper passively listens to and intercepts the confidential information sent by sensors. To minimize the average AoI of the system, a joint AAV trajectory and IRS phase shift optimization problem is formulated under the constraints of covertness requirement. The minimum error detection probability and optimal detection threshold are first derived at Willie, which represents the worst case situation for the legitimate transmission. The constructed problem is a mixed integer programming NP-Hard problem, which is more complex using traditional convex optimization methods. Therefore, the online learning, i.e., deep reinforcement learning is leveraged to obtain the near-optimal solution. Details, the deep Q network (DQN) and deep deterministic policy gradient (DDPG) methods are utilized. Numerical results show that deep reinforcement learning can improve the information freshness of the system by reasonably designing AAV trajectory and IRS phase shift under a given covertness constraint.

This paper investigates a multi-user uplink mobile edge computing (MEC) network, where the users offload partial tasks securely to an access point under the non-orthogonal multiple access policy with the aid of a reconfigurable intelligent surface (RIS) against a multi-antenna eavesdropper. We formulate a non-convex optimization problem of minimizing the total energy consumption subject to secure offloading requirement, and we build an efficient block coordinate descent framework to iteratively optimize the number of local computation bits and transmit power at the users, the RIS phase shifts, and the multi-user detection matrix at the access point. Specifically, we successively adopt successive convex approximation, semi-definite programming, and semidefinite relaxation to solve the problem with perfect eavesdropper’s channel state information (CSI), and we then employ S-procedure and penalty convex-concave to achieve robust design for the imperfect CSI case. We provide extensive numerical results to validate the convergence and effectiveness of the proposed algorithms. We demonstrate that RIS plays a significant role in realizing a secure and energy-efficient MEC network, and deploying a well-designed RIS can save energy consumption by up to 60% compared to that without RIS. We further reveal impacts of various key factors on the secrecy energy efficiency, including RIS element number and deployment position, user number, task scale and duration, and CSI imperfection.

Frequency diverse array (FDA) is a promising antenna technology to achieve physical layer security by varying the frequency of each antenna at the transmitter. However, when the channels of the legitimate user and eavesdropper are highly correlated, FDA is limited by the frequency constraint and cannot provide satisfactory security performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where the positions of antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the secrecy capacity by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on block successive upper-bound minimization (BSUM) method. Moreover, to evaluate the security performance provided by MFDA, we introduce two benchmark schemes, i.e., phased array (PA) and FDA. Simulation results demonstrate that MFDA can significantly enhance security performance compared to PA and FDA. In particular, when the frequency constraint is strict, MFDA can further increase the secrecy capacity by adjusting the positions of antennas instead of the frequencies.

The frequency diverse array (FDA) is highly promising for improving covert communication performance by adjusting the frequency of each antenna at the transmitter. However, when faced with the cases of multiple wardens and highly correlated channels, FDA is limited by the frequency constraint and cannot provide satisfactory covert performance. In this paper, we propose a novel movable FDA (MFDA) antenna technology where positions of the antennas can be dynamically adjusted in a given finite region. Specifically, we aim to maximize the covert rate by jointly optimizing the antenna beamforming vector, antenna frequency vector and antenna position vector. To solve this non-convex optimization problem with coupled variables, we develop a two-stage alternating optimization (AO) algorithm based on the block successive upper-bound minimization (BSUM) method. Moreover, considering the challenge of obtaining perfect channel state information (CSI) at multiple wardens, we study the case of imperfect CSI. Simulation results demonstrate that MFDA can significantly enhance covert performance compared to the conventional FDA. In particular, when the frequency constraint is strict, MFDA can further increase the covert rate by adjusting the positions of antennas instead of the frequencies.

In this paper, we analyse the reliability performance for the orthogonal time frequency space (OTFS) based low earth orbit (LEO)-satellite-air-terrestrial (LSAT) communication system. To facilitate the downlink transmission from the LEO satellite to the terrestrial node, a group of randomly distributed mobile unmanned aerial vehicles (UAVs) are employed to serve as the relays with decode-and-forward (DF) scheme. With the aid of stochastic geometry approach, the distribution of mobile UAVs is modeled by Poisson point processes (PPP) process with two motion modes: user dependent model (UDM) and user independent model (UIM). We derive the approximate closed-form expressions for the outage probabilities of the LSAT system under two UAV motion modes. Finally, the simulation results demonstrate that the reliability of the LSAT system can be significantly enhanced by using OTFS scheme, and by properly adjusting the UAV deployment parameters, corroborating the theoretical derivation.

Reconfigurable intelligent surface (RIS) is a key potential technology for the sixth generation wireless communication. The deployment of RIS in wideband communication systems can effectively mitigate severe path loss and against the blockage of line-of-sight path, which can improve transmission gain and enhance communication quality. However, with the increase of RIS array and bandwidth, beam-squint effect will occur and seriously damage the performance of communication systems. In this paper, we first establish a holographic RIS-aided wideband communication system model from the perspective of the electromagnetic wave propagation theory. Then, we analyze the holographic RIS electromagnetic characteristics under the beam-squint effect. Further, we derive the angle spread range, 3dB beam bandwidth, and beam coverage range to analyze the regularities of beam offset. Besides, we propose a new codebook design scheme to address the impact of the beam-squint effect. Finally, we introduce the true-time-delay (TTD) lines into the holographic RIS structure to mitigate the beam-squint effect. The simulation results reveal the influence of the beam-squint, and also verify the mitigation effect of TTD lines on the beam-squint effect.

Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) are considered promising technologies for the sixth generation (6G) wireless communication. The deployment of RIS within the mmWave ISAC system can achieve better communication performance and sensing accuracy. The mmWave band signals can be utilized to enhance transmission rates and available bandwidth significantly. However, the increased size of the RIS array and bandwidth introduces the beam-squint effect, which impacts the performance of RIS-aided communication and sensing. In this paper, we analyze the beam-squint and beam-split effects on a uniform planar array of RIS. Moreover, we derive controllable beam-squint and beam-split ranges based on true-time-delay (TTD) lines and propose RIS-aided sensing schemes with beam-squint and beam-split for a mmWave ISAC system. The proposed schemes can utilize both time-domain and frequency-domain resources for beam scanning, which reduces the time overhead compared to traditional beam scanning schemes. Simulation results illustrate the effectiveness of the proposed RIS-aided user sensing schemes.

Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.

Millimeter wave (mmW) is a promising technology for the next generation of mobile communications. However, the transmission efficiency and communication reliability of heterogeneous mobility user networks are limited by the frequent mmW beam alignment and the severe Doppler shift in the time-varying channel, respectively. To address this challenge, a joint resource allocation in frequency domain, time domain and power domain is investigated for the mmW communication network based on non-orthogonal multiple access (NOMA) and orthogonal time-frequency space (OTFS) techniques. The average spectral efficiency of high-mobility user equipment (H-UE) is maximized by the joint optimization of UE scheduling, beamwidth and transmit power. In order to solve the non-convex mixed integer problem of rate maximization, we propose the multi-dimensional resource allocation scheme based on alternate optimization method (AO-MRA), which decouples the initial intractable problem into two solvable sub-problems. In particular, based on the majorization-minimization approach, the scheduling algorithm is proposed to find the best UE scheduling for the NOMA groupings of heterogeneous mobility UEs, and the joint beamwidth and transmit power (JBP) algorithm is further designed for the optimal transmission time and power of base station in the mmW communication network. The proposed AO-MRA scheme can obtain effective suboptimal solutions of the initial problem. Simulation results demonstrate that the AO-MRA scheme is superior to other benchmark schemes in maximizing transmission efficiency. Moreover, the AO-MRA scheme is more suitable for low-power and high-bandwidth situations, and has superior spectral efficiency in terms of delay and Doppler high-resolution.

The existing methods of hyperspectral image (HSI) and multispectral image (MSI) fusion usually overlook the fact that multi-source images acquired under different imaging conditions are generally not perfectly registered. Despite the many such methods that have begun to address registration issues, it is still a challenge that most works perform registration and fusion as two separate steps, resulting in a cumulative error. To address this challenge, we propose a progressive registration-fusion co-optimization A-Mamba network (PRFCoAM), which iteratively optimizes the modal-aligned progressively registration-fusion (MAPRF) module to adaptively corrects the deformation from an extensive to a detailed level and refines the fusion results at each level to achieve progressive registration-fusion co-optimization. The proposed MAPRF module integrates the modal unified local aware registration (MULAR) block and interactive attention Mamba fusion (IAMF) block, which facilitates the network comprehensively and efficiently capture features of different levels. Specifically, MULAR adaptively learns spectral and spatial degradation functions to transform the input images into a unified modality and progressively repairs non-rigid pixel offsets by capturing the correlations and differences between corresponding regions of images. IAMF multi-directionally scans the spatial and spectral global dependent features of the well-registered images, which can stimulate the potential of Mamba in fusion and achieve a win-win situation of computational efficiency and selectivity advantages in the global acceptance domain. Extensive experiments demonstrate PRFCoAM can flexibly deal with different degrees and kinds of non-rigid deformation and achieves state-of-the-art performance. The code will be available at https://github.com/Jiahuiqu/PRFCoAM-for-HSI-MSI-Registration-Fusion.

We propose a joint covert beamforming design and jamming strategy to protect the communication process between Alice and Bob from being discovered by Willie with the help of another pair of neutral nodes. Specifically, with the help of irrelevant communication parties that commonly exist in practical communication scenarios, Jammer increases his transmission power by interfering with the neutral receiver, thus indirectly increasing the interference to Willie, which can be viewed as leveraging the force to make a big impact with a small effect. In our designed beamformer, we jointly optimize the beam power allocation factor, Alice’s transmission power, and Jammer’s transmission power when Alice transmits, to maximize the covert rate, which also maximizes Alice’s transmission power. In the perfect channel state information (CSI) scenario, the transformed optimization problem is solved via a one-dimensional search method and CVX solver. Due to the solution’s high complexity, we further propose a method to determine the optimal power allocation factor. For the imperfect Willie’s CSI scenario, three cases are investigated: Alice to Willie imperfect CSI, Transmitter to Willie imperfect CSI, and Jammer to Willie imperfect CSI. We utilize the S-procedure to tackle the optimization problem. Simulation results demonstrate the effectiveness of our proposed strategy.

Few-shot learning (FSL) has been rapidly developed in the hyperspectral image (HSI) classification, potentially eliminating time-consuming and costly labeled data acquisition requirements. Effective feature embedding is empirically significant in FSL methods, which is still challenging for the HSI with rich spectral-spatial information. In addition, compared with inductive FSL, transductive models typically perform better as they explicitly leverage the statistics in the query set. To this end, we devise a transductive FSL framework with enhanced spectral-spatial embedding (TEFSL) to fully exploit the limited prior information available. First, to improve the informative features and suppress the redundant ones contained in the HSI, we devise an attentive feature embedding network (AFEN) comprising a channel calibration module (CCM). Next, a meta-feature interaction module (MFIM) is designed to optimize the support and query features by learning adaptive co-attention using convolutional filters. During inference, we propose an iterative graph-based prototype refinement scheme (iGPRS) to achieve test-time adaptation, making the class centers more representative in a transductive learning manner. Extensive experimental results on four standard benchmarks demonstrate the superiority of our model with various handfuls (i.e., from 1 to 5) labeled samples. The code will be available online at https://github.com/B-Xi/TIP_2025_TEFSL.

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.

Jamming recognition plays a pivotal role in anti-jamming communication system, ensuring an effective anti-jamming strategy can be given quickly. Because the characteristics of jamming are manifested in multiple domains, fully extracting the characteristics of jamming can improve the accuracy of jamming recognition. Therefore, a multi-modal feature extraction network for jamming recognition is constructed in this paper. The network leverages both time-frequency diagram containing time-frequency domain information and cyclic spectrum diagram containing frequency domain information of jamming as inputs. By designing a multi-scale convolutional neural network, the long-range dependencies of the time-frequency diagram and the fine features of the cyclic spectrum are captured. To ensure the full fusion of features, the feature map matrix of each channel after network processing is subjected to Hadamard product, and then the sum of the results is averaged as the final feature vector. Simulation results demonstrate that the proposed method achieves an average recognition rate of 99.5% when the Jamming to Signal Ratio (JSR) exceeds 0dB.

Internet of Things (IoT) technology that enables the interconnection of ubiquitous end devices through wireless networks, has penetrated into every aspect of our lives. With the popularity of Unmanned Aerial Vehicles (UAVs), UAV can help IoT application by improving coverage, assisting in data collection, reducing equipment costs and so on. This paper focuses on the security challenges of UAV s conducting covert communication in IoT networks. It considers the exposure of the UAV in IoT, which can not only lead to communication failure but also result in potential attacks on the UAV. To address these challenges, we jointly optimize the transmit power and flying location of the UAV, aiming to maximize its communication quality while ensuring communication covertness. The final simulations illustrate the process of obtaining optimal solutions for the UAV's transmit power and flying location. Furthermore, they validate the effectiveness of the proposed scheme, which achieves maximum received SNR while satisfying the covertness constraint.

As the mainstay of the Internet of Things (IoT), data carries the vital memory of the future intelligent society. However, because of the inherent broadcast and open characteristics of wireless channels, transmitting data securely and reliably is a huge challenge for IoT. In this paper, a covert transmission control scheme with high reliability and anti-jamming capability is proposed to deal with the above challenges, by exploiting the artificial noise emitted by cooperative interference sources in the environment. Specifically, with the aim of maximizing the warden's detection error probability, the transmit power and frequency are jointly optimized under the constraints of transmission reliability and channel equivocation. The Block Coordinate Descent (BCD) algorithm is used to solve the formulated optimization problem. Numerical results demonstrate the superiority of our proposed scheme, which can simultaneously achieve high reliability, covertness, and anti-jamming capability.

This work investigates a cognitive satellite-terrestrial covert communication network, where a satellite serves as the primary transmitter to communicate with a primary user (PU) covertly under the surveillance of an unauthorized warden and a terrestrial base station (BS) serves as the secondary transmitter to communicate with a secondary user. Considering imperfect channel state information (CSI) of warden, we jointly design both satellite's transmit power and BS's transmit beamforming to maximize the PU's covert rate while satisfying the terrestrial network's information rate requirement, satellite network's covertness and both satellite's and BS's power constraints. The successive convex approximation method is employed to obtain the optimal BS beamforming. In addition, the imperfect CSI is tackled by the$S$-procedure technique. Simulation results reveal that compared to other benchmark schemes the proposed optimization algorithm performs better in terms of the covert rate.

In order to overcome the vicious competition between different high-volume services, we study the wireless resource sharing problem in the transmission process of the MEC-caching-coexist (MCCe) system with the capability of mmWave communications. The multiobjective Markov decision process (MOMDP) is introduced to model the task scheduling and resource allocation problem for the mmWave links, which aims to minimize the transmission delay and energy consumption simultaneously. Note that, for practical consideration, the exact channel information of all links are not known. We propose a novel multiobjective deep reinforcement learning with discrete-continuous hybrid action space (MODRL/HA) algorithm. In particular, the envelope updated design (EUD) is designed to realize the multiobjective optimization from the perspective of the Bellman operator. On the other hand, the parameterized network design (PND) is developed to deal with the hybrid action space of discrete task scheduling and continuous beamwidth and power variables. Our simulations show that, the MODRL/HA algorithm can improve 22% performance in terms of the tradeoff between delay and energy consumption compared with the benchmark schemes, which are original deep deterministic policy gradient (DDPG) and multiobjective DDPG (MODDPG) algorithms.

This paper investigates a dual-unmanned aerial vehicle (UAV) assisted short packet covert communication system in the presence of a warden. Specifically, one flying UAV serves as the base station to transmit covert information to a legitimate ground user, and the other flying UAV is deployed as a cooperative jammer to transmit artificial noise against detection by a warden. Considering a more practical scenario, where only imperfect location information of the warden is known at both UAVs, we jointly optimize both UAVs’ transmit powers and three-dimensional (3D) trajectories to maximize the average covert transmission rate under the constraints of both UAVs’ mobility, transmit powers and warden’s detection error probability (DEP). On the one hand, the incomplete Gamma function involved in warden’s DEP makes the covertness constraint intractable for further analysis. To facilitate the design, warden’s DEP is lower bounded by Pinsker’s inequality. On the other hand, the formulated optimization problem is intractable to solve directly owing to the multiple highly coupled variables and the uncertainty of the warden’s location. The alternating optimization algorithm combined with the successive convex approximation and S-procedure techniques is leveraged to solve three optimization subproblems iteratively. Numerical results reveal that superior performance can be achieved by employing the proposed joint optimization of both UAVs’ transmit powers and 3D trajectories algorithm compared with the traditional two-dimensional trajectory optimization algorithm and the scheme without the assistance of the cooperative UAV.

Spectrum is the fundamental medium for transmitting information services, including communication, navigation, and detection. Spectrum anomalies can lead to substantial economic losses and even endanger life safety. Anomaly detection constitutes a critical component of spectrum risk management. Through spectrum anomaly detection (SAD), anomalous spectrum usage behaviors, such as malicious user activities, can be identified. Given the significant limitations of current SAD algorithms in terms of accuracy and localization capabilities, this article proposes an approach for detecting spectral anomalies that utilizes knowledge distillation and memory-enhanced autoencoders (AEs). First, the pretrained network with robust feature extraction capabilities is distilled into the teacher network. Subsequently, both an AE and a memory-enhanced AE with an identical structure are trained to predict the teacher network’s normalized outputs on a spectrum devoid of anomalies. Finally, in the case of an anomalous spectrum, difference exist between the normalized outputs of the teacher network and the outputs of different student networks, as well as among the outputs of different student networks, which facilitates the process of anomaly detection. The outcomes of experiments reveal that the proposed algorithm is more effective on both synthetic spectral data sets and real IQ signals, demonstrating its proficiency in accurately detecting and locating anomalies.

Due to the openness of wireless channels, wireless communication is vulnerable to be eavesdropped, which results in confidential information leakage. Physical Layer security (PLS) technology provides a new way to solve this hidden danger of Internet of Things system. However, traditional PLS methods are often restricted by limited communication resources and unknown instantaneous channel state information of eavesdroppers, which makes it challenging to strike a balance between security and reliability in the communication system. Therefore, an adversarial defense embedded waveform design (ADEWD) method for physical layer reliable communication (PLRC) is proposed in this paper. Firstly, we use generative adversarial networks to generate amplitude controllable adversarial perturbation, and then superimpose it with original communication signal to form an adversarial signal. At the same time, we also design a demodulation network based on the modulation type of legitimate users to constrain the amplitude of the generated perturbations, to reduce the bit error rate (BER) loss after demodulation of the adversarial signal. With this waveform design, the adversarial signal not only enables reliable communication between legitimate users, but also utilizes embedded defense traps to prevent eavesdroppers from recognizing legitimate users. The experimental results demonstrate that our ADEWD method for PLRC has stronger defense capability and lower BER in both white-box and black-box scenarios, which reflects the defense robustness and communication reliability of the proposed waveform design method.

Internet of Things (IoT) play a paramount role in every aspect of our daily lives. Due to more diverse human needs, the future IoT networks are expected to be highly dynamic and heterogeneous with assistance of mobile nodes, such as unmanned aerial vehicles (UAVs). However, the broadcast and openness nature of wireless communication and high-mobile characteristic of UAV can cause security threats to UAV-assisted IoT systems. For this sake, we consider exploiting covert communication to provide such a system with a higher level communication security, which can prevent the legitimate transmission being detected by the adversary monitor. Specifically, this article jointly optimizes the transmit power and hovering location of the UAV to guarantee communication security in the IoT system. We maximize the signal-to-noise ratio (SNR) of the legitimate receiver in presence of a malicious warden, with constraints of communication covertness, the UAV’s spatial location and maximum transmit power. Particularly, the UAV’s location is represented in terms of angles, rather than the commonly used distance, in most of the literature. The optimal location is determined in two steps. The simulation results show that the proposed optimization schemes can effectively find the optimal hovering location and transmit power of the UAV to maximize the receiver’s SNR under the covertness constraint. The optimal hovering location is directly above the line connecting the legitimate receiver and the warden, and in close proximity within the small region directly above the receiver, which has implications for other analogous research scenarios or practical applications of UAV.

This paper investigates the optimization of the probabilistically robust transmit beamforming problem with channel uncertainties in the multiuser multiple-input single-output (MISO) downlink transmission. This problem poses significant analytical and computational challenges. Currently, the state-of-the-art optimization method relies on convex restrictions as tractable approximations to ensure robustness against Gaussian channel uncertainties. However, this method not only exhibits high computational complexity and suffers from the rank relaxation issue but also yields conservative solutions. In this paper, we propose an unsupervised deep learning-based approach that incorporates the sampling of channel uncertainties in the training process to optimize the probabilistic system performance. We introduce a model-driven learning approach that defines a new beamforming structure with trainable parameters to account for channel uncertainties. Additionally, we employ a graph neural network to efficiently infer the key beamforming parameters. We successfully apply this approach to the minimum rate quantile maximization problem subject to outage and total power constraints. Furthermore, we propose a bisection search method to address the more challenging power minimization problem with probabilistic rate constraints by leveraging the aforementioned approach. Numerical results confirm that our approach achieves non-conservative robust performance, higher data rates, greater power efficiency, and faster execution compared to state-of-the-art optimization methods.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure for the ISAC scheme is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication service. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Recently, cross-scene hyperspectral image classification (HSIC) has attracted increasing attention, alleviating the dilemma of no labeled samples in the target domain. Although collaborative source and target training has dominated this field, training effective feature extractors and overcoming intractable domain gaps remains challenging. To cope with this issue, we propose a multi-level unsupervised domain adaptation (MLUDA) framework, which comprises image-, feature-, and logic-level alignment between domains to fully investigate the comprehensive spectral-spatial information. Specifically, at the image level, we propose an innovative domain adaptation method named GuidedPGC based on classic image matching techniques and the guided filter. The adaptation results are physically explainable with intuitive visual observations. Regarding the feature level, we design a multi-branch cross attention structure (MBCA) specifically for HSIC, which enhances the interaction between the features from the source and target domains through dot-product attention. Finally, at the logic level, we adopt a supervised contrastive learning (SCL) approach that incorporates a pseudo-label strategy and local maximum mean discrepancy loss, increasing inter-class distance across diverse domains and further improving the classification performance. Experimental results on three benchmark cross-scene datasets demonstrate that our proposed method consistently outperforms the compared approaches. The source code is available at https://github.com/cfcys/MLUDA.

Few-shot learning (FSL) has rapidly advanced in the hyperspectral image classification (HSIC), potentially reducing the need for laborious and expensive labeled data collection. Due to the limited receptive field, the convolutional neural network (CNN) struggles to capture long-range dependencies for extracting global features. Additionally, the transformer focuses on global correlation while overlooking the effective representation of local spatial and spectral features. Moreover, contrastive learning (CL) has emerged as a powerful technique for improving consistency across different augmented views of samples of the same category. To this end, we devise a novel CNN-Transformer for few-shot HSIC assisted by semisupervised contrastive learning, named CTF-SSCL, to boost the classification performance. Specifically, the cascaded CNN-Transformer incorporates a lightweight spatial-spectral interactive convolution module (LSSICM) and a multiscale transformer (MSFormer) to exploit local features from submaps and global information from the entire patch. Subsequently, the semisupervised contrastive loss, comprising unsupervised and supervised components, serves as an auxiliary to optimize the model with the classification loss. Wherein, recognizing the unified spectral-spatial information in HSI, we propose a spectral feature shift strategy (SFSS) to create sample pairs for the unsupervised CL, utilizing unsupervised contrastive loss among groups of samples with identical labels. Extensive experiments on four standard benchmarks demonstrate the effectiveness of the proposed CTF-SSCL with varying amounts of labeled samples. The code will be available online athttps://github.com/B-Xi/CTF-SSCL.

Wireless information surveillance has received widespread attention due to the urgency of monitoring growing suspicious communications. This paper considers a challenging surveillance scenario, where the monitor (E) intends to eavesdrop the suspicious multihop communications from a long distance to ensure concealment, leading to the eavesdropping condition undesirable. To tackle this challenging, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted surveillance strategy, where the STAR-RIS, acts as a “bridge”, is deliberately deployed between the suspicious system and E, to adaptively transmit and reflect the suspicious signal and E's jamming signal, and then facilitate E's eavesdropping. Specifically, we consider the adaptive rate transmission and the delay-limited transmission for the suspicious system, and accordingly maximize E's instantaneous and average eavesdropping rate, by jointly optimizing the passive transmission- and reflection-coefficient matrices at the STAR-RIS, the jamming set and jamming power allocations of E (across all hops). The optimization problems in both transmission modes include numerous integer and continuous variables and thus are highly non-convex. Nevertheless, we show by detailed analysis that the original problem in each mode can be solved by only considering two possible cases, where E and the STAR-RIS intend to enhance and reduce the suspicious transmission rate, respectively. More importantly, in each case, many of necessary prerequisites for achieving the optimal solution are first determined analytically. Armed with these, the optimization problem then can be solved by leveraging the successive convex approximation technique and the simple search. As demonstrated by simulation results, since our proposed strategy is adaptive in term of varying the suspicious transmission rate, it will achieve significant eavesdropping performance gain as compared to other competitive benchmarks.

This paper investigates the data collection problem for multi-functional unmanned aerial vehicle (UAV) swarm in a dynamic wireless sensor network (WSN), where sensors have different mobility profiles. For a practical consideration, the observation information of the UAVs is limited, and has the risk of obsolescence, under the limited battery life. The considered optimization problem is formulated as a partially observable Markov decision process (POMDP), which includes the discrete on-off variables of collection, radar, communication and movement, and the continuous variables of the transmit power, UAV flying direction and velocity. For solving the problem, we propose a multi-agent hybrid proximal policy with reward shaping and pre-training optimization algorithm (MAHPPO-RSP). In particular, the proposed algorithm is performed through a two-step training way of supervised learning and reinforcement learning, upon introducing both human experience and autonomous learning. The provided results show that the proposed MAHPPO-RSP algorithm exhibits a stable convergence manner. Furthermore, it obtains a promising trade-off between data collection and energy consumption, outperforming two baseline schemes.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

This paper investigates a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. Three efficient operating protocols for the S-IOS, namely time switching, power splitting, and mode switching, are proposed to enable the dual-functionality of energy harvesting and information transmission. For each protocol, we design a joint optimization framework of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the network sum rate. Despite the challenging non-convex optimization problems with highly coupled and/or integer optimization variables, we develop computationally-efficient algorithms to solve them in an iterative manner, which exploit the intrinsic structure of the problems and employ the penalty-based method and the successive convex approximation. Numerical results confirm the efficiency of our developed optimization algorithms, demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications, and quantify the performance advantage of the proposed designs over the baseline schemes.

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

Non-terrestrial networks (NTN) provide a revolutionary solution to bridge the digital divide in areas underserved by terrestrial network (TN). Particularly, low Earth orbit (LEO) constellations can substitute for offering data services to mobile devices like UAVs when flying into TN service-deficient areas. In this paper, viewing TN and NTN as both competitors and collaborators, we present a novel approach to optimize UAV mobility management in integrated TN and NTN, thereby improving network service continuity. Specifically, we enable UAVs to opportunistically handover (HO) between TN and NTN during flight to maintain reliable data reception while minimizing HO overhead. The decision to switch from TN to NTN involves comparative assessments of service capabilities and HO rates between two segments over time, considering their link quality variations during UAV flight, TN coverage distributions, and orbital dynamics of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant advantages of UAV HO planning in integrated TN and NTN over standalone TN or NTN for HO numbers and service rates. We also demonstrate that in various scenarios, our UAV mobility management solution consistently outperforms existing heterogeneous HO methods that underrate the dynamic differences in service capabilities between TN and NTN.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle to transmit and reflect the signal from the base station (BS). Firstly, an efficient transmission structure for the ISAC scheme is designed. Then, the time-frequency selective BS-RIS-RSUs channel model are characterized. Based on the estimated cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links, the vehicle localization and its velocity can be acquired. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are designed for performance enhancememt. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

In this paper, we study a covert communication strategy based on non-ideal detection on overt channels in Internet of Things (IoT) networks, where IoT device utilizes existing overt channels as spectrum masks to achieve covert communication. We consider the non-ideal detection of IoT device on overt channels. At the same time, to improve covert transmission rate, we use improper Gaussian signaling (IGS) at IoT device. We first provide detection errors of IoT device, and then analyze the non-ideal transmission rate using IGS and minimum error detection probability of the warden. Next, we jointly optimize the transmit power of IoT device and circularity coefficient of IGS to maximize the covert rate while meeting the quality of service (QoS) constraint of the overt channel and the covertness constraint. The final simulations demonstrate that considering the non-ideal detection of IoT device and utilizing IGS can effectively improve the system transmission rate.

This paper focuses on reducing the effect of systematic uncertainties on the near-field or far-field source direction-finding accuracy by introducing a calibration emitter, which suffers the same uncertainties as the actual source. We propose a modified polar representation (MPR)-based closed-form algebraic algorithm, i.e., the improved successive unconstrained minimization (SUM), to eliminate the common errors of time difference of arrival (TDOA) measurements, thereby refining the source direction-finding accuracy. The simulations show that the proposed algorithm can approach the Cramer–Rao Lower Bound´ (CRLB); and demonstrate the positive effect of the calibration emitter on the direction-finding performance under the condition of varying measurement error, source range, and sensor position error.

Abstract Blind watermark extraction in discrete cosine transform (DCT) domain has a wide application prospect as well as a challenging subject. The imperceptibility of watermark signal makes watermark extraction a weak signal reception issue in essence. For DCT coefficients of host image generally disobey Gaussian distribution, at which the performance of linear correlated reception is no longer optimal. Aiming at this, a novel blind watermark extraction scheme combining the uncorrelated reception with adaptive bistable stochastic resonance (ABSR) technique is proposed. First, by block DCT transformation for host image, an additive watermark embedding algorithm is introduced, in which the watermarked image can be converted to one dimensional time domain weak signal (binary watermark image) reception under additive Laplacian noise (selected DCT coefficients). On this basis, through the key technology research on quantitatively cooperative resonance relationship under Laplacian noise, the ABSR system can be implemented by bistable system parameters self‐adaptive adjustment, in which the ABSR system output signal will be enhanced rather than be weakened by random noise. Finally, the ABSR‐based watermark extraction scheme is investigated, and both the visual effect, bit error ratio performance and robustness of proposed scheme are testified to be superior to that of traditional uncorrelated extraction.

Upon exploiting massive spectrum resources, millimeter-wave (mmW) communication can significantly improve the transmission rate of mobile-edge computing (MEC) offloading, whereas the directional mmW links are constrained by shrunk beam coverage and demand extra phase for beam alignment. To enhance the accessing efficiency, we develop the nonorthogonal multiple access (NOMA) scheme-based mmW MEC mechanism, namely, NOMA-mmW MEC, therefore motivating to minimize the average delay of the MEC offloading, by jointly optimizing the beamwidth, user equipment (UE) scheduling, and transmit power. To tackle the mixed-integer nonlinear programming (MINLP) problem of delay minimization, we develop the alternative optimization (AO) approach-based RA scheme, namely, AO-RA, to obtain the close-optimum solutions. In the AO-RA scheme, we propose the matrix control many-to-one with externality (MC-M2OE) algorithm, to find the best UE scheduling for the NOMA groupings of different types of UEs. Upon the above, we further design the joint beamwidth and transmit power (JBTP) algorithm, which determines the optimal beamwidth and transmit power for the MEC offloading transmissions. Our simulation results show the effectiveness of the proposed AO-RA scheme in minimizing the offloading delay, where our MC-M2OE and JBTP algorithms can significantly outperform the existing approaches. From the simulation results, we may conclude that it needs to carefully address the tradeoff between beam alignment overhead and transmission gain while properly balancing the loading among different NOMA groups, for the practical consideration of NOMA-mmW MEC technology.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ$\epsilon $-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

Covert transmission is investigated for a cooperative deception strategy, where a cooperative jammer (Jammer) tries to attract a multi-antenna adversary (Willie) and degrade the adversary’s reception ability for the signal from a transmitter (Alice). For this strategy, we formulate an optimization problem to maximize the covert rate when three different types of channel state information (CSI) are available. The total power is optimally allocated between Alice and Jammer subject to the Kullback-Leibler (KL) divergence constraint, which can be expressed analytically and be widely used as a covertness measurement. Different from the existing literature, in our proposed strategy, we also determine the optimal transmission power at the jammer when Alice is silent, while existing works always assume that the jammer’s power is fixed. Specifically, we apply the S-procedure to convert infinite constraints into linear-matrix-inequalities (LMI) constraints. When statistical CSI at Willie is available, we convert double integration to single integration using asymptotic approximation and substitution method. Finally, our simulation results show that for the proposed strategy, the covert rate is increased with the number of antennas at Willie. Moreover, compared to the benchmark, our proposed strategy is more robust in the presence of imperfect CSI.

In recent years, hyperspectral image classification (HSIC) has achieved impressive progress with emerging studies on deep learning models. However, the classification performance downgrades due to the limited number of annotated samples, especially for minority classes. Notably, the imbalanced data dilemma is familiar in remote sensing hyperspectral image because the ground objects are commonly distributed without evenness. Therefore, this paper proposes a novel deep generative spectral-spatial classifier (DGSSC) for addressing the issues of imbalanced HSIC. Specifically, the DGSSC comprises three components, a two-stage encoder, a decoder, and a classifier, which are trained in an end-to-end manner. In particular, to exploit the abundant spectral-spatial features with relatively low computational complexity, the first stage of the encoder comprises successive three-dimensional (3D) and two-dimensional (2D) convolutions, exploring the spectral-spatial and deep spatial information. In addition, the second stage involves the deep latent variable model to achieve minority-class data augmentation. Furthermore, a patch distance-based reconstruction loss function is designed to facilitate the outputs of the decoder being more similar to the input 3D patch samples. The proposed DGSSC can outperform the state-of-the-art methods on three benchmark datasets, especially with its more robust prediction results. For instance, the DGSSC achieves a remarkable 97.85% mean overall accuracy with 0.24% standard deviation over ten independent runs with randomly selected imbalanced 1% training samples on the University of Pavia dataset.

Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.

This paper investigates a cognitive information surveillance scenario, where a half-duplex legitimate monitor (E) aims to eavesdrop the frequency division multiple access based suspicious downlink broadcasting communication, which shares the same spectrum with a primary downlink broadcasting system. Specifically, the suspicious transmitter (ST) employs water-filling power allocation over all orthogonal frequency bands (FBs) to maximize the sum suspicious communication rate of all suspicious receivers (SRs), while the primary transmitter (PT) purposely maximizes the minimum communication rate among the primary receivers (PRs). Under this setup, E picks up certain FBs to jam and eavesdrops over the remaining FBs, and intends to control its transmit jamming beamforming over the jammed FBs to deliberately interfere with the corresponding PRs and SRs, such that the PT and the ST will reactively reallocate their power to facilitate E's eavesdropping. Our objective is to maximize the sum instantaneous (ergodic) eavesdropping rate at E, by jointly optimizing its jamming set and the corresponding jamming beamformers. The optimization problem is non-convex and its approximate and sub-optimal solutions are provided by some convex optimization procedures, along with the optimal and greedy jamming set selections. Results show the effectiveness of our proposed jamming-assisted eavesdropping compared to competitive benchmarks.

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

Spectrum sensing with transmit power identification can greatly facilitate the application of the hybrid spectrum access strategy in cognitive radio (CR) networks. Conventional model-driven methods suffer from severe performance degradation in low signal-to-noise ratio (SNR) regime. In this paper, we propose a multiple transmit power levels identification network (TPIN) which consists of three components. In the data preprocessing components, the covariance matrix (COV) of the received data is first employed as the observation data. Then, the residual network (ResNet) based feature extractor components is used to construct the test statistic by extracting high-dimensional features of the observation data. Furthermore, the likelihood ratio test (LRT) criterion is leveraged to design the cost function for obtaining the maximum posterior probability in the classifier components. Different from the assumption in conventional method, the prior probability of each transmit power levels is unknown to the TPIN, and the array of training set is randomly disturbed. In addition, in order to verify the ability of TPIN in data features extraction, a comparison reference experiment using a general test statistic (e.g., higher-order cumulative) as the observation data is introduced. Finally, simulation results demonstrate the identification performance of the COV-based (COV-TPIN) scheme.

Covert communication is able to provide high-level security by protecting communication behavior. In this paper, we develop a covert cooperative cognitive radio (CCCR) network, where primary transmitter (PT) transmits information with the aid of multiple secondary transmitters (STs). In return, STs are able to transmit private information by exploiting PT's spectrum in presence of a powerful eavesdropper (Eve). Meanwhile, we propose a cognitive user scheduling scheme based on link information and maximum-minimum principle. Moreover, we derive Eve's expected detection error probability and evaluate the covert performance of the novel scheme. Numerical results show that joint impact of self-interference and jamming power of Eve can enable STs to achieve covert transmission. Furthermore, it can be found that the influence of the interference power on Eve's detection error probability and covert performance is significant when the self-interference cancellation coefficient is sufficient large.

This paper focuses on improving the sensor selection performance in the time difference of arrival (TDOA)-based localization scenario with the presence of source estimation error. In existing schemes, a coarse source position is first estimated and regarded as the actual counterpart for selecting optimal sensors. However, if the estimated position deviates from the actual position, the localization accuracy determined by the selected sensor subset will severely degrade. To solve the issue, we devise a distance-related weighted average Cramér Rao lower bound (WA-CRLB) to include the spatial information of the actual position by scattering sampling points around the estimated source position according to its distribution. Then, we formulate a Boolean vector-based sensor selection optimization problem to minimize WA-CRLB and propose a modified iterative swapping greedy (MISG) algorithm. Simulation results show that the proposed MISG algorithm achieves higher localization robustness with the increase of TDOA measurement error strength, and has lower computational complexity compared with the previous semi-definite relaxation (SDR)-based algorithms.

In this paper, we study the information freshness on covert communication in the Internet of Things (IoT) networks. The freshness of information is characterized by a recently introduced metric, termed as age of information (AoI). Specifically, without a feedback channel, each packet generated at the transmitter is only allowed to be transmitted during one time slot no matter whether it is successfully decoded at the receiver. In this case, the average AoI at the receiver and the average probability of error detection at the warden are derived. Then, the transmit power is optimized to minimize the AoI while guaranteeing the covertness requirement. On the other hand, with a perfect feedback channel, packet re-transmission is adopted to make the information fresh enough. The average AoI and the average probability of error detection are analyzed. Then, the transmit power is also optimized in this case. Simulation results reveal that the proposed scheme can minimize average AoI under the requirement of covertness, and the case using re-transmission with feedback achieves a lower AoI under the same requirement of covertness.

This article investigates the covert performance of an unmanned aerial vehicle (UAV) jammer-assisted cognitive radio (CR) network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of an eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN)-assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then, a GAN-based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of the UAV’s trajectory and transmits power to maximize the covert rate and the probability of detection errors. Our simulation results show that the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of the UAV’s trajectory and transmit power for the covert communication.

In this work, we aim to maximize the timeliness of data collection subject to a covertness constraint in unmanned aerial vehicle (UAV)-aided Internet of Things (IoT) networks, where a UAV periodically conducts wireless power transfer (WPT) to charge an energy-constrained IoT device and then the IoT device opportunistically sends its collected data to the UAV. To this end, we first derive a lower bound on the covertness constraint and an analytical expression for Age of Information (AoI) to characterize timeliness. Then, we jointly optimize the UAV’s transmit power for WPT, the WPT duration, and the data transmission duration by considering two practical scenarios. For the fixed total duration scenario, our analytical optimal solutions indicate that the total harvested energy at the IoT device is independent of the covertness constraint, although both the UAV’s transmit power and the WPT duration are significantly affected by the covertness constraint. With the optimized total duration scenario, we prove that the UAV’s optimal transmit power is always attained at its maximum value regardless of the existence of the covertness constraint, but the WPT and data transmission durations are sensitive to the required covertness. Overall, there exists a nontrivial tradeoff between the timeliness and covertness for data collection in the considered system, which is determined by the WPT design. Furthermore, our numerical results show that the optimal prior probability of the IoT device’s opportunistic transmission is generally not 0.5 for the fixed total duration, but it is indeed 0.5 for the optimized total duration.

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the$\epsilon $-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

This paper investigates the security performance of a joint unicast-multicast streaming system, where different users present heterogeneous mobilities. The orthogonal time frequency space (OTFS) scheme is employed to overcome severe Doppler effect caused by high mobility. The closed-form expression is derived for the maximum secrecy rate of unicast transmission with high privacy. Furthermore, the positive secure capacity probability (PSCP) of unicast transmission is also obtained and analyzed. Our analytical results show that compared with high-mobility eavesdroppers, low-mobility eavesdroppers pose a greater threat to unicast secrecy. Moreover, when the outage probability of unicast is greater than 1/2, more time frequency (TF) resources should be allocated to unicast, in order to guarantee the security performance of unicast.

Hyperspectral anomaly target detection (also known as hyperspectral anomaly detection (HAD)] is a technique aiming to identify samples with atypical spectra. Although some density estimation-based methods have been developed, they may suffer from two issues: 1) separated two-stage optimization with inconsistent objective functions makes the representation learning model fail to dig out characterization customized for HAD and 2) incapability of learning a low-dimensional representation that preserves the inherent information from the original high-dimensional spectral space. To address these problems, we propose a novel end-to-end local invariant autoencoding density estimation (E2E-LIADE) model. To satisfy the assumption on the manifold, the E2E-LIADE introduces a local invariant autoencoder (LIA) to capture the intrinsic low-dimensional manifold embedded in the original space. Augmented low-dimensional representation (ALDR) can be generated by concatenating the local invariant constrained by a graph regularizer and the reconstruction error. In particular, an end-to-end (E2E) multidistance measure, including mean-squared error (MSE) and orthogonal projection divergence (OPD), is imposed on the LIA with respect to hyperspectral data. More important, E2E-LIADE simultaneously optimizes the ALDR of the LIA and a density estimation network in an E2E manner to avoid the model being trapped in a local optimum, resulting in an energy map in which each pixel represents a negative log likelihood for the spectrum. Finally, a postprocessing procedure is conducted on the energy map to suppress the background. The experimental results demonstrate that compared to the state of the art, the proposed E2E-LIADE offers more satisfactory performance.

Hyperspectral anomaly detection (HAD) is a challenging task since samples are unavailable for training. Although unsupervised learning methods have been developed, they often train the model using an original hyperspectral image (HSI) and require retraining on different HSIs, which may limit the feasibility of HAD methods in practical applications. To tackle this problem, we propose a dual-frequency autoencoder (DFAE) detection model in which the original HSI is transformed into high-frequency components (HFCs) and low-frequency components (LFCs) before detection. A novel spectral rectification is first proposed to alleviate the spectral variation problem and generate the LFCs of HSI. Meanwhile, the HFCs are extracted by the Laplacian operator. Subsequently, the proposed DFAE model is learned to detect anomalies from the LFCs and HFCs in parallel. Finally, the learned model is well-generalized for anomaly detection from other hyperspectral datasets. While breaking the dilemma of limited generalization in the sample-free HAD task, the proposed DFAE can enhance the background–anomaly separability, providing a better performance gain. Experiments on real datasets demonstrate that the DFAE method exhibits competitive performance compared with other advanced HAD methods.

We investigate covert wireless communication in a multi-antenna relay network, where the relay transmits its own covert message to the destination when assisting the source’s information delivery, and the source acts as a warden to detect this covert transmission. Based on whether the channel state information of the relay-destination link is available at the source or not, we propose two relay beamforming schemes, namely random beamforming and maximum-ratio transmission (MRT) beamforming schemes, to guarantee the reception reliability at the destination while deliberately introducing uncertainty to the source to degrade its detection. Under the worst-case covert communication scenario where the source is capable of optimizing its detection threshold, analytical expressions for the minimum detection error probability achieved by each of the proposed schemes are derived to evaluate the detection limits of the source. By utilizing the above analytical results as the covertness constraint, an optimization problem of transmit power allocation for each scheme is formulated and solved to maximize the covert rate. The impact of imperfect channel state information on the covert communication performance is also examined. Simulation results are performed to confirm the accuracy of the derived analytical results and quantify the communication covertness enhancement of the proposed schemes. Our results also show that the MRT beamforming scheme offers a higher covert rate than that of the random beamforming scheme, especially when the covertness constraint becomes loose and/or the number of antennas at the relay increases.

In this paper, we propose a covert wireless uplink transmission strategy in space-air-ground integrated vehicular networks, where the source vehicle transmits its own message over the channel that being used by the host communication system, to avoid being detected by the warden. It is obvious that the data transmission efficiency of the covert communication system is limited due to the co-channel interference. To improve the data transmission efficiency, we consider that the covert communication system adopts improper Gaussian signaling (IGS). We formulate a joint transmit power and IGS factor optimization problem to minimize the outage probability of the covert communication system. The minimum error detection probability of the warden is first analyzed with noise uncertainty, which is used to measure the system covertness. Under the constraints of the quality of service (QoS) of host communication system and the covertness requirement, the optimal transmit power is first derived with proper Gaussian signaling (PGS) scheme. Then, with the approximate outage probability derived under IGS scheme, the optimization problem is solved by jointly designing the transmit power and IGS factor. Finally, we provide extensive numerical results to validate the proposed covert transmission strategy, and demonstrate that the IGS scheme is beneficial in improving the data transmission efficiency in terms of outage probability compared to PGS scheme.

Advances in adversarial attack and defense technologies will enhance the reliability of deep learning (DL) systems spirally. Most existing adversarial attack methods make overly ideal assumptions, which creates the illusion that the DL system can be attacked simply and has restricted the further improvement on DL systems. To perform practical adversarial attacks, a detection tolerant black-box adversarial-attack (DTBA) method against DL-based automatic modulation classification (AMC) is presented in this article. In the DTBA method, the local DL model as a substitution of the remote target DL model is trained first. The training dataset is generated by an attacker, labeled by the target model, and augmented by Jacobian transformation. Then, the conventional gradient attack method is utilized to generate adversarial attack examples toward the local DL model. Moreover, before launching attack to the target model, the local model estimates the misclassification probability of the perturbed examples in advance and deletes those invalid adversarial examples. Compared with related attack methods of different criteria on public datasets, the DTBA method can reduce the attack cost while increasing the rate of successful attack. Adversarial attack transferability of the proposed method on the target model has increased by more than 20%. The DTBA method will be suitable for launching flexible and effective black-box adversarial attacks against DL-based AMC systems.

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

In this paper, we consider the problem of full-duplex covert millimeter wave (mmWave) communications, where a mmWave transmitter (Alice) sends information signals to its intended receiver (Bob) covertly in the presence of a watchful warden (Willie). For covering the presence of Alice, Bob operates in the full-duplex mode and generates jamming signals with a time-varying power. We investigate the covert rate optimization for both the single data stream case and the multiple data streams case under the constraints of the detection error probability at Willie. Specifically, for the single data stream case, we analytically characterize the minimum detection error probability at Willie and establish a framework for optimizing the analog beamforming, transmit power, and analog jamming jointly. As for the case of multiple data streams, we derive a tractable lower bound of the minimum detection error probability at Willie and formulate a joint optimization of the hybrid precoder and analog jamming design problem for the maximization of the achievable covert rate. Although the joint design problem is nonconvex, we adopt the penalty decomposition technique to handle the effect of the coupling between the analog precoder and digital precoder paving the way for the development of an iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we show that our proposed joint design algorithm can be adapted to handle the multi-antenna Willie scenario and simulation results show that our proposed joint design algorithms can achieve significantly better performance as compared with some benchmark schemes.

Considering multi-user downlink ultra-high reliability and low latency communications (URLLC), this paper employs the artificial noise (AN) technique to establish a secure and covert broadcast communication paradigm for the first time. Specifically, a multi-antenna transmitter (Alice) broadcasts the confidential information to multiple legitimate users in the presence of a multi-antenna malicious warden (Willie) and a multi-antenna eavesdropper (Eve). It is well known that AN is an effective technique for securing the physical layer security (PLS) of signal transmissions. Nevertheless, AN emission also exposes the signal transmission and decreases the signal covertness. Taking into account the impact of short-packet URLLC transmissions, we investigate the joint optimization of the precoder and AN to maximize the secrecy rate under the covertness constraint. Although the considered problem is nonconvex, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve it optimally. However, the nested-loop structure of the BRB-based algorithm incurs a high computational complexity. To strike a balance between the performance and computational complexity, we also propose a low-complexity penalty successive convex approximation (SCA)-based algorithm, whose performance approaches that of the optimal BRB-based algorithm, particularly in the low to medium transmit power regime. Simulation results demonstrate the excellent performance of our proposed optimization algorithms compared with various benchmark algorithms and unveil the importance of exploiting AN for secrecy provisioning.

To realize secure communication and prevent eaves-droppers from detecting the existence of communication activities, covert communication has attracted substantial research interests. In this paper, we propose a dynamic spectrum control (DSC)-assisted scheme to achieve covert and reliable data transmission. Specifically, by constructing time-frequency division channels, the proposed DSC-assisted scheme generates sequences with iterative and orthogonal transformations. Authorized users can orderly occupy different frequency slots in each time slot under the guidance of these sequences, thus achieving simultaneous data transmission without interfering with each other. Then, the covert performance of the proposed transmission scheme is analyzed to provide the closed-form expressions of covert transmission rate and the reliable transmission probability. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed scheme can achieve better covert and reliable transmission performances when compared with the existing scheme.

Covert communication, which can hide the communication behavior, has great potential in guaranteeing the security of information and transmission terminal to the greatest extent. In this paper, we propose a covert communication strategy based on improper Gaussian signaling (IGS) to increase the covert rate in the Internet of Things (IoT) system, in which the existing signals emitted by other transmitters were used as “Spectrum Shelter” to cover the IoT's communication. Specifically, we analyze the system's achievable transmission rate when the IoT node adopts IGS. Then, the optimal detection threshold and the minimum error detection probability of the warden are analyzed. Next, by jointly optimizing the transmission power and circularity coefficient of IGS, we maximize the covert rate under the constraints of the covertness. Finally, the simulation results verify that the proposed scheme can not only guarantee the covertness, but also improve the achievable covert rate.

This paper considers the physical layer security (PLS) and covertness of the signal transmission in a multiple-input single-output downlink adopting ultra-high reliability and low latency communication (URLLC). In the considered system, Alice transmits confidential signals to Bob in the presence of a multi-antenna eavesdropper (Eve) and a multi-antenna watchful adversary (Willie). Although artificial noise (AN) is a common PLS technique for protecting the confidential signal from wiretapping, it may reduce the communication covertness due to the additional signal emission. For maximizing the achievable secrecy rate, we propose an AN-aided secure and covert communication strategy through optimizing the information carrying beamformer and AN jointly subject to the covertness constraint. To tackle the formulated non-convex design problem, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve the considered problem globally. Simulation results validate its efficiency compared with a benchmark algorithm and unveil the importance of exploiting AN.

In this paper, we propose an improper Gaussian signaling (IGS) empowered covert communication strategy in unmanned air vehicle (UAV) assisted communication systems. The ground user (Alice) covertly transmits confidential messages to the UAV amounted based station (Bob) by superimposing over an overt channel, which is licensed to an existing communication system. To alleviate the inner-system interference caused by the superimposed waveforms, we design the IGS as the waveform of the covert communication system and the proper Gaussian signaling (PGS) as the waveform of the existing communication system. We derive closed-form expressions for the outage prob-ability of the overt and covert transmission links, respectively. Moreover, we formulate a joint transmit power and IGS factor optimization problem to maximize the outage performance of the covert communication system constraining the covertness requirement and quality of service of the existing communication system. The optimal solution is derived by leveraging the mono-tonic properties of objective function and constraints. Finally, simulation results are provided to verify the effectiveness of the proposed covert communication strategy.

Covert communication achieved by using the full-duplex (FD) receiver has wide applications. Specifically, the FD receiver can emit artificial noise to prevent the signal of transmitter from detecting by the illegitimate warden. In this paper, we investigate the robust joint power and position optimization (JPPO) for the full-duplex receiver (i.e., Bob) in the presence of the uncertainties of the warden (i.e., Willie). We first analyze the effect of the warden's position uncertainty on the performance of covert communication. Then, by using robust optimization technique, we maximize the effective covert throughput of transceivers by optimizing the power and position of the Bob constraining the sufficient covertness requirement. Theoretical analysis indicate that the effective covert throughput between Alice and Bob is inversely proportional to the distance between them within the deployable zone (DZ) satisfying the covert condition, and the optimal position of Bob is on the boundary of the DZ near Alice. Finally, simulation results verified our conjecture.

As a promising architecture to provide ubiquitous and ultra-reliable network connectivity, space-air-ground integrated network (SAGIN) has attracted substantial research interests. In this paper, we propose a dynamic spectrum access control (DSAC) protocol for the SAGIN. Specifically, the proposed DSAC protocol uses sequences to represent the spectrum access decisions for authorized users at different time slots. Through iterative and orthogonal sequence transformation, the DSAC protocol can generate orthogonalized sequences to guarantee successful spectrum access for authorized users. In addition, the non-collision probability of the data packets accessing the shared spectrum under the guidance of DSAC protocol is analyzed, based on which a closed-form expression of the system throughput is further derived. Simulation results are provided to validate the accuracy of the theoretical analysis and demonstrate that the proposed protocol is effective in access control and throughput improvement in the SAGIN when compared with the existing random access protocol.

Abstract This work deals with the problem of insufficient energy caused by the frequent use of relay nodes in wireless sensor networks. Here, energy cooperation relay selection algorithm is proposed in energy heterogeneous networks. It is assumed that the location of relay nodes obeys a homogeneous Poisson point process (PPP), and the relay nodes adopt an adaptive energy collection technology, which can harvest energy from the surrounding environment and decide whether to use or store it according to the current energy status of the battery. The relay nodes are divided into three kinds: the communication relay, the candidate relay and the ordinary relay. The candidate relay nodes are determined by K‐medoids clustering algorithms and the ordinary relay nodes transmit energy to them. Finally, the number of data packets sent by relay nodes and the expression of the energy status of the battery are derived correspond to the relay cooperation selection algorithm. The simulation results show that the energy cooperation relay selection scheme effectively extends lifetime of the network when the total network energy is relatively small.

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper's channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper's imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD's information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush-Kuhn-Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

With unprecedented progress on Internet of Things (IoT), spectrum scarcity becomes even severe with the explosive growth of wireless smart devices. To deal with spectrum scarcity issues, cognitive radio (CR)-enabled IoT has been emerged as a promising solution, which allows IoT devices reusing the underutilized spectrum bands. In this article, we investigate spectrum sensing in CR-IoT, in which full-duplex CR-IoT node can perform spectrum sensing and data transmission concurrently for reducing sensing delay. Two sensing methods are proposed based on multiple high-order cumulants for excavating rich information of the non-Gaussian transmitted signals. Specifically, for the scenarios with a single sensing antenna, we first propose a multiple high-order cumulants-based sensing method (MCS) derived from the likelihood ratio test, which is assumed to be near optimum. The test statistics are derived, respectively, in two cases, i.e., the case only performing sensing and the case performing sensing and transmission simultaneously. Interestingly, the derived two test statistics have same expression, while the corresponding sensing thresholds are different from each other. For the scenarios with multiple sensing antennas, we propose a multiantenna-assisted multiple high-order cumulants-based sensing method (MMCS), which can provide a tradeoff between the computational complexity and sensing performance. We conduct the hypothesis test with Hotelling's T2-statistic and derive the corresponding sensing threshold. Theoretical performance evaluated by detection probability and computational complexity of the proposed methods are analyzed. Additionally, extensive simulations are provided, which show both the proposed methods can counter the adverse effects of noise uncertainty, and MCS has superiority over MMCS in terms of sensing accuracy.

Accurate location estimation of Internet-of-Things (IoT) devices within an Area of Interest (AoI) is a challenging issue, especially in a global navigation satellite system (GNSS)-constrained environment. In this article, we present a space-air integrated localization network (SAILN) architecture to exploit the advantages of the unmanned-aerial-vehicle (UAV)-based localization through joint position and power optimization (JPPO) strategies. In SAILN, UAVs can utilize their flexible movement to obtain the line-of-sight (LOS) path with a high probability, thereby providing the potential IoT devices in the AoI with supplementary localization information. The JPPO of UAVs aims to improve the regional localization accuracy for the entire AoI, considering the no-fly-zone (NFZ) and the total energy constraint. We propose the average localization accuracy increment (ALAI) of the sampling points in the AoI as the metric to measure the performance of SAILN compared with that of only satellites, which is regarded as the objective to formulate the JPPO problems for UAV operations in both static and dynamic SAILN. The intractable problems can be resolved by the pure genetic algorithm (PGA) that has a low computational cost and unique features suiting the JPPO of UAVs. Then, by taking advantage of the ALAI convexity to the UAVs' power, we propose a power reallocation-based two-step algorithm (PRTSA) to further explore an improved JPPO solution. Simulation results validate that the proposed PRTSA can obtain a higher localization accuracy for the entire AoI than the PGA and the other straightforward baselines.

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie's link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie's link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob.

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

In this paper, we study covert communications between a pair of legitimate transmitter-receiver against a watchful warden over slow fading channels. There coexist multiple friendly helper nodes who are willing to protect the covert communication from being detected by the warden. We propose an uncoordinated jammer selection scheme where those helpers whose instantaneous channel gains to the legitimate receiver fall below a pre-established selection threshold will be chosen as jammers radiating jamming signals to defeat the warden. By doing so, the detection accuracy of the warden is expected to be severely degraded while the desired covert communication is rarely affected. We then jointly design the optimal selection threshold and message transmission rate for maximizing covert throughput under the premise that the detection error of the warden exceeds a certain level. Numerical results are presented to validate our theoretical analyses. It is shown that the multi-jammer assisted covert communication outperforms the conventional single-jammer method in terms of covert throughput, and the maximal covert throughput improves significantly as the total number of helpers increases, which demonstrates the validity and superiority of our proposed scheme.

This paper studies covert millimeter-wave (mmWave) communications, where a multi-antenna transmitter (Alice) sends information signals to a full-duplex receiver (Bob) covertly, in the presence of a warden (Willie). For covering the communication by Alice, Bob operates in full-duplex mode which generates jamming signals with a transmit power varying across different time slots. Assuming that Willie adopts a radiometer as its detector, we first derive the optimal detection threshold for Willie. Next, we jointly design the analog beamforming at Alice and the analog jamming at Bob for maximizing the covert rate taking into account the use of optimal detecting at Willie and the communication outage probability experienced by Bob. Although the joint design is nonconvex that is challenging to solve directly, a successive convex approximation algorithm-based algorithm is developed to address the design problem. Simulation results validate the efficiency of the proposed algorithm, compared to some baseline scheme.

In this paper, we investigate a NOMA assisted multi-antenna two-way relay system, where users apply NOMA to support bidirectional superposition transmission with the aid of multiple relays. Specifically, we propose a multiple-access broadcast NOMA strategy together with a joint antenna-and-relay selection scheme to enhance its transmission reliability. Analytical closed-form expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed strategy with the joint antenna-and-relay selection scheme. Based on the analytical result, we further optimize the power allocation to reduce the outage probability. Our numerical results show that the proposed mechanism significantly outperforms existing benchmark strategies in terms of the outage probability.

In this paper, we propose a multiobjective optimization framework to jointly optimize power and position of full-duplex (FD) receiver in the covert communication. By introducing a legitimate FD receiver (Bob) with random transmit power, the signal of a legitimate transmitter (Alice) can be transmitted covertly since an eavesdropper (Willie) is confronted with interference uncertainty and makes an incorrect decision for signal detection. Therefore, we optimize the position and the transmit power range of Bob in order to maximize the achievable transmission rate from Alice to Bob and average covert probability at Willie simultaneously. Due to the presence of multiple optimization objectives, the nondominated sorting genetic algorithm II (NSGA-II) is utilized to explore the Pareto front and to give a set of solutions that reveal different tradeoffs between the two conflicting objectives. Simulation results are provided to reveal the Pareto front and to illustrate the effect of transmit power of Alice and Bob on the Pareto front.

Covert communication provides high-level security for protecting users' privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

Unmanned aerial vehicles (UAVs)‐based sensor network is an effective mechanism for recognising and tracking of manoeuvring targets as well as expanding the monitoring coverage in the battlefield. However, there exists redundancy among the spectrum data collected by a UAV monitor within a data collection period, which may waste storage space and reduce the speed of data uploaded to the control centre. The authors assume that each UAV is equipped with an edge computing server and propose a delta compression method, which can save cache space and transfer time. First, they present a cost model and evaluation model for delta compression of the COPY/ADD class. Then an optimisation problem is formulated aiming to obtain the optimal delta encoding. Additionally, a maximal total length of copied fragments (MTLC) algorithm is proposed to find more mutually separated L ‐grams common fragments between the data collected at two adjacent moments. Theoretical analysis proves that the MTLC algorithm can generate a good delta encoding with the maximum total length and then the minimum total number of the COPYs. Moreover, numerical results show that MTLC has better performance in constructing a good delta encoding than the simple greedy and hash suffix array delta algorithms.

To explore the potential of machine learning‐based cooperative spectrum sensing (CSS) in training time, classification speed and classification performance, this study mainly focuses on studying the problem of the feature vectors selecting for machine learning‐based CSS. First, a new machine learning‐based CSS framework is presented, in which, energy vector forming module, feature vector conversion module, training module, classification module and training sample database are included. Second, a new two‐dimensional distance vector is developed, and it is converted by an m ‐dimensional energy vector according to the distance measurement between vectors. Furthermore, six combination modes are obtained by combining three feature vectors (energy, probability and distance vectors) with two supervised machine learning methods, which are support vector machine (SVM) and weighted K‐nearest‐neighbour, respectively. From the proposed experimental simulations, the authors can find that the distance vector is obviously superior to the probability vector in computation time. Moreover, the probability vector and distance vector are superior to the energy vector in training time except for the case of poor signal and fewer users, and obviously superior to the energy vector in classification speed. At last, the probability vector and distance vector with SVM classifier show the best classification performance in six combination modes.

Secrecy transmission is investigated for a cooperative jamming scheme, where a multi-antenna jammer generates artificial noise (AN) to confuse eavesdroppers. Two kinds of eavesdroppers are considered: passive eavesdroppers who only overhear the legitimate information, and active eavesdroppers who not only overhear the legitimate information but also jam the legitimate signal. Existing works only treat the passive and active eavesdroppers separately. Different from the existing works, we investigate the achievable secrecy rate in presence of both active and passive eavesdroppers. For the considered system model, we assume that the instantaneous channel state information (CSI) of the active eavesdroppers is available at the jammer, while only partial CSI of the passive eavesdroppers is available at the jammer. A new zero-forcing beamforming scheme is proposed in the presence of both active and passive eavesdroppers. For both the perfect and imperfect CSI cases, the total transmission power allocation between the information and AN signals is optimized to maximize the achievable secrecy rate. Numerical results show that imperfect CSI between the jammer and the legitimate receiver will do more harm to the achievable secrecy rate than imperfect CSI between the jammer and the active eavesdropper.

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems with a fully-adaptive-connected precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the energy efficiency (EE) of hybrid precoding under this structure, the joint optimization of switch-controlled connections and the hybrid precoders is formulated as a large-scale mixed-integer non-convex problem with high-dimensional power constraints. To efficiently solve such a challenging problem, we first decouple it into a continuous hybrid precoding (CHP) subproblem. Then, with the hybrid precoders obtained from the CHP subproblem, the original problem can be equivalently reformulated as a discrete connection-state (DCS) problem with only 0-1 integer variables. For the CHP subproblem, we propose an alternating hybrid precoding (AHP) algorithm. Then, with the hybrid precoders provided by the AHP algorithm, we develop a matching assisted fully-adaptive hybrid precoding (MA-FAHP) algorithm to solve the DCS problem. It is theoretically shown that the proposed MA-FAHP algorithm always converges to a stable solution with the polynomial complexity. Finally, simulation results demonstrate the superior performance of the proposed MA-FAHP algorithm in terms of EE and beampattern.

Massive malicious jamming devices and advanced jamming techniques have been emerged along with the development of wireless communication networks. In order to effectively eliminate the harmful interference, a new scheme known as the cognitive frequency hopping (CHF) is proposed recently, which can evaluate the occupancy of frequency hopping slots and adjust the parameters dynamically according to the spectrum sensing results. Although the existing literature only shows that CFH systems can achieve reliable data transmission, the factors affecting the reliability are rarely analyzed. Therefore, we analyze the reliability performance of the CHF systems in this article, and define a new metric named anti-interference tolerance to measure the reliability performance of the CHF systems. Moreover, we derive the analytic expression of anti-interference tolerance by analyzing the effect of false alarm probability, missed detection probability, and communication link convergence delay. Simulation results validate the effectiveness of our analyses for measuring the reliability capacity of the CHF systems. To satisfy the demands of different communication scenarios, the CFH systems can adjust relevant parameters in the light of our theoretical derivation.

In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted multi-antenna two-way relay system, where multi-antenna users apply NOMA to support bidirectional superposition transmission via multiple multi-antenna relays. Specifically, we propose two cooperative strategies, namely multiple-access broadcast NOMA and time division broadcast NOMA. For each of the two cooperative strategies, we devise a joint antenna-and-relay selection scheme to enhance the transmission reliability. Analytical expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed cooperative strategies with the corresponding joint antenna-and-relay selection schemes. To further reduce the outage probability, we use the derived analytical results as objective functions to optimize the transmit power allocation under both cooperative strategies. Finally, extensive simulations are carried out to validate the accuracy of the derived analytical results. Our simulation results indicate that the proposed strategies significantly outperform existing benchmark strategies in terms of outage probability and diversity order.

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

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

A cooperative jamming and beamforming scheme is proposed for secrecy transmission. Different from the existing works, an internal eavesdropper (Eve) and multiple external Eves coexist in the system, where a multi-antenna jammer is deployed to confuse Eves by using artificial noise. Specifically, the jammer can obtain the instantaneous channel state information (CSI) of the internal Eve, but only has the partial CSI of the external Eves. Upon setting up, the transmission outage requirement at Bob and secrecy outage constraint at the Eves, the transmit power is optimized to derive the maximum secrecy rate for the transmission. Moreover, we investigate the optimal power allocation scheme by characterizing the beamforming vectors under considering both perfect and imperfect CSI cases for the internal Eve. Numerical results show that the proposed scheme can improve the secrecy rate significantly by making full use of instantaneous CSI between the jammer and the internal Eve.

Unmanned aerial vehicles (UAV) deployment is of pivotal importance in the promising space-air integrated localization network (SAILN), which is a typical partially controllable network and supports 3- dimensional (3D) localization. To improve the localization accuracy for specific area or user, several UAVs need to be deployed. This paper proposes an iterative UAV deployment strategy for SAILN, which can minimize the localization error by determining accurate 3D coordinate information (elevation and azimuth angles, distance) for all the supplementary UAVs. Specifically, based on the analysis of accuracy increment when a new UAV is added into SAILN, the genetic algorithm (GA) is leveraged to find its optimal geometric position in a constrained area that can maximize the accuracy increment. Then, UAVs are iteratively added until the desired number, i.e., the quantity budget of deployed UAVs, is achieved. Simulation results demonstrate that the proposed UAV deployment strategy provides considerably better localization accuracy compared with uniform angular arrays (UAA) and random deployment (RD).

This paper investigates the sensor selection problem for time difference of arrival (TDOA) localization in wireless sensor networks. Specifically, a multi-objective optimization problem is formulated in which a Boolean vector is involved to find the best tradeoff between the localization accuracy and the energy consumption. The ε- constraints method is introduced to convert the original multi- objective optimization problem to a tractable single-objective problem. To solve the converted sensor selection problem, we propose the polyblock outer approximation (POA) algorithm based on discrete monotonic optimization (DMO) in order to find the global optimal solution, which however can not be obtained by the traditional semidefinite relaxation (SDR) approach. Further, for the sake of practical implementation, we propose another two suboptimal algorithms, namely, POA-based accelerated cutting (POA-AC) algorithm and POA-based monotonic cutting (POA-MC) algorithm. Simulation results validate that the localization accuracy for sensors selected by the POA-AC algorithm and POA-MC algorithm is greater than the semidefinite relaxation (SDR) solution and achieves the same results as that by the exhaustive search method.

Orbital angular momentum (OAM) has attracted much attention for radio vortex wireless communications due to the orthogonality among different OAM-modes. To maintain the orthogonality among different OAM modes at the receiver, the strict alignment between transmit and receive antennas is highly demanded. However, it is not practical to guarantee the transceiver alignment in wireless communications. The phase turbulence, resulting from the misaligned transceivers, leads to serious intermode interference among different OAM modes and therefore fail for signals detection of multiple OAM modes at the receiver. To achieve practical OAM based wireless communications, in this paper we investigate the radio vortex wireless communications with misaligned transmit and receive antennas. We propose a joint Beamforming and Pre-detection (BePre) scheme, which uses two unitary matrices to convert the channel matrix into the equivalent circulant matrix for keeping the orthogonality among OAM-modes at the receiver. Then, the OAM signals can be detected with the mode-decomposition scheme at the misaligned receiver. Extensive simulations obtained validate and evaluate that our developed joint BePre scheme can efficiently detect the signals of multiple OAM-modes for the misaligned transceiver and can significantly increase the spectrum efficiency.

In this paper, when transmit antenna selection (TAS) and maximal ratio combining (MRC) schemes are deployed for secrecy enhancement, the impact of channel correlation on secrecy performance is investigated over Rayleigh fading channels. Specially, both the exact and asymptotic secrecy outage probability (SOP) for TAS/MRC are derived based on a novel correlation model, where the correlated legitimate channels and eavesdropper channels are modeled as a set of conditional independent channel gains. Moreover, the lower bound of SOP is obtained in high signal-to-noise ratio (SNR) region. Finally, simulation results verify our analysis, and show that the channel correlation between the legitimate receiver and the eavesdropper is beneficial to the SOP at high SNR.

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems. To jointly consider the spectral efficiency and energy consumption, we propose an adaptive hybrid precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the spectral-and-energy efficiency under this structure, the joint optimization of the on-off states for switch-controlled connections and the hybrid precoding matrices is formulated as a non-convex problem. To efficiently solve this problem, we first propose an alternative limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) based algorithm to determine the hybrid precoders. Then, using the alternative L-BFGS algorithm, a greedy algorithm is proposed to jointly optimize the hybrid precoders and the on-off states of switch-controlled connections. It is theoretically shown that, this alternative L-BFGS based algorithm always converges to a stationary point. Further, the convergence of proposed greedy algorithm is also theoretically proved and validated by simulations. Simulation results also demonstrate that the proposed hybrid precoding achieves a superior tradeoff between the spectral efficiency and energy consumption.

This paper investigates the resource allocation in millimeter wave ultra dense networks, in which the beam assignment and sub-band allocation are jointly considered. Motivating to maximize the sum rate of the network conceived, the optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem, which involves allocating the novel three-dimensional resource blocks (RBs) defined in beam (B), time (T), and frequency (F) dimension, respectively. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the so-called best option first (BOF) beam assignment algorithm, and the many-to-one matching with externalities (M2O-ME) sub-band allocation algorithm. In particular, the BOF beam assignment algorithm is first carried out to coordinate the RBs in terms of T- and B-dimension. Then, with the aid of the mechanism of many-to-one with externalities, the M2O-ME sub-band algorithm is implemented to find the optimal sub-band allocation (i.e. RB allocation in F-dimension) solution. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for practical ultra dense small-cell networks with mmWave capability.

Internet of Vehicle (IoV) is a promising Internet of Thing (IoT) application, where roadside unit (RSU) plays an important role to transmit traffic information to cloud server through internet. However, there exists redundancy among the data collected with a data collection period by a single vehicle, which may cause network congestion and waste the storage of cloud. In this paper, we propose to equip each RSU with a mobile edge computing (MEC) server, and differential compress data at edge node, for saving the transmission time and storage space. First, we present a new cost model for COPY/ADD class, then a metric to measure a differential compression algorithm is given. we propose a Maximal Length of COPYs (MLOC) algorithm that can construct a good delta encoding based on our cost model and evaluation. Theoretical analysis proves that the proposed MLOC algorithm constructs a delta encoding with minimal amount of COPYs on the premise of maximal total length of data segments copied. Numerical results show that MLOC algorithm can get better performance in constructing a good delta encoding between two data compared with Simple Greedy algorithm and Hash Suffix Array Delta (Hsadelta) algorithm.

In order to improve the signal‐to‐noise ratio (SNR) gain and lower the bit error rate (BER) when a weak signal is buried in the noise, the parameter‐tuned stochastic resonator (PSR) is studied in more detail. In this study, the authors explore the impact of the number of branches on performance. Hence, they build the parameter‐tuned bistable parallel array system, which consists of a group of PSRs and propose a novel method to derive the probability density function (PDF) of the output signal, which combines the conventional PDF based on the approximation theory with Gaussian distribution. Based on this method, an analytical expression for BER of the system is derived. It is of interest to note that for an input binary pulse amplitude modulation signal buried in noise, the weak signal can be detected by using the parameter‐tuned bistable parallel array system. It is found that via the array PSR effect, the system output SNR gain can be improved. Moreover, the BER can also be reduced with the increase of the array size as the array size increases.

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multiantenna transceiver design and multihop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. First, multihop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Second, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the nonconvexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters.

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA.

In this paper, considering both the antenna correlation and the channel correlation between the legitimate receiver and the eavesdropper, we comprehensively investigate the secrecy performance for transmit antenna selection and maximal ratio combining (TAS/MRC) scheme in a multi-input multi-output multi-antenna eavesdropper (MIMOME) system. Different from the existing works, the correlated legitimate channels and correlated eavesdropper channels experience Nakagmai-${m}$fading with distinct fading parameters, and are modeled as a combination of conditionally independent channel gains and independent channel gains. By this novel correlation representation, the combined effect of antenna correlation and channel correlation between the legitimate receiver and the eavesdropper on secrecy performance is quantified via the secrecy outage probability (SOP) under a more general fading circumstance. In addition, two special cases of correlated channels are addressed in detail. Finally, simulations are performed to verify our analysis. The analysis and simulation results show that at high signal-to-noise ratio (SNR), antenna correlation at the legitimate receiver can improve the SOP performance for the correlated MIMOME wiretap channels, and this finding is contrary to the conventional wisdom that antenna correlation degrades the SOP performance.

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. The sum rate is maximized by jointly optimizing the UAV trajectory and the NOMA precoding. To solve the optimization, we decompose it into two steps. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained using two schemes with different constraints. The first scheme intends to cancel the interference from the BS to the UAV-served user, while the second one restricts the interference to a given threshold. In both schemes, the non-convex optimization problems are converted into tractable ones. An iterative algorithm is designed. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.

Rigid body localization (RBL) extends the traditional point positioning by determining not only the position but also the orientation of the body. This paper considers RBL using the range measurements between the sensors on the body and the outside anchors that have position uncertainties, where a calibration emitter at an inaccurate location is employed to mitigate the anchor position errors. This is a highly nonlinear constrained optimization problem as the rotation matrix defining the orientation must belong to the special orthogonal group. We first propose the use of rotation angles to parameterize the orientation, which enables the reduction of the problem to an unconstrained optimization. It is then sufficient to consider the unconstrained Cramér-Rao Lower Bound (CRLB) for analyzing the effects of anchor position errors and noisy calibration position on the localization performance, and such an analysis is prohibitive when using the constrained CRLB. We next propose and analyze three Maximum Likelihood estimators obtained from the unconstrained formulation that have different levels of approximation and complexity. We also enhance the divide and conquer and the semi-definite programming solution from the literature such that they can work with the presence of a calibration emitter and account for anchor position errors.

This paper presents a detailed secrecy performance analysis of correlated multi-antenna wiretap channels with three popular diversity combining schemes, namely maximal ratio combining, selection combining, and equal gain combining at the legitimate receiver. For single-input multiple-output wiretap channels, both exact and asymptotic expressions are derived for the secrecy outage probability (SOP) of these systems. The findings suggest that, compared with the scenario where all the channels are independent, correlation of the main channels alone increases the SOP by a factor of 1/ det(U), where det(U) is the determinant of correlation matrix U. In contrast, when the average SNR at the legitimate receiver is much larger than that at the eavesdropper, correlation between the main channels and the eavesdropper channels has a positive effect on SOP. Moreover, in the case of multiple-input and multiple-output wiretap channels, two transmit antenna selection schemes with or without the eavesdropper's channel state information are evaluated by the SOP and the average secrecy capacity for the considered correlated channel model, respectively. Finally, numerical simulations are conducted to corroborate the analytical results.

In this paper, we investigate the physical layer security of cooperative two-way relay transmission systems using the amplify-and-forward (AF) protocol in the presence of an eavesdropper. A joint relay selection (RS) and power allocation (PA) scheme is proposed to protect the source-destination transmission against the eavesdropper. However, due to the high computational complexity, it is difficult to obtain the optimal solution for the system secrecy rate. Fortunately, an approximate optimal solution by using the particle swarm optimization (PSO) algorithm is derived. In the simulations, we use random relay selection with optimal power allocation (RRS-OPA) and equal power allocation with optimal relay selection (EPA-ORS) as benchmark schemes to verify the effectiveness of the proposed method. The simulation results show that the proposed method outperforms both RRS-OPA and EPA-ORS and significantly improves the system performance with low complexity.

Recently, ambient backscatter has been introduced as an attractive technology that allows small devices, such as battery-less sensors and passive tags, to communicate by using radio-frequency (RF) signals over the air. It is worth noting that multiple-antenna tags could perform energy harvesting and backscattering simultaneously and thus are advantageous for ambient backscatter communication systems. Therefore, in this paper, we consider the ambient backscatter communication systems with multiple-antenna tags and focus on signal detection problem. Multiple antennas imply multiple channel parameters, which are difficult to estimate because the tags have limited power and can transmit few training symbols. To address this problem, a semi-blind detector is designed for readers to recover tag signals without any knowledge of the multiple channel parameters between the reader and the tag. We also derive the bounds on the detection probabilities. Moreover, an antenna selection scheme is suggested to optimize the detection performance. Finally, simulation results are provided to corroborate our theoretical studies.

Small-cell network is a promising solution to high video traffic. However, with the increasing number of mobile devices, it cannot meet the requirements from all users. Thus, we propose a caching device-to-device (D2D) scheme for small-cell networks, in which caching placement and D2D establishment are combined. In this scheme, a limited cache is equipped at each user, and the popular files can be prefetched at the local cache during off-peak period. Thus, dense D2D connections can be established during peak time aided by these cached users. To do this, first, an optimal caching scheme is formulated according to the popularity to maximize the total offloading probability of the D2D system. Then, the sum rate of D2D links is analyzed in different signal-to-noise ratio (SNR) regions. Furthermore, three D2D-link scheduling schemes are proposed with the help of bipartite graph theory and Kuhn-Munkres algorithm for low, high and medium SNRs, respectively. Simulation results are presented to show the effectiveness of the proposed scheme.

Secrecy performance is investigated for relay networks with multiple antennas Nt at the transmitter. As K relays are available, we propose an incremental threshold relaying scheme with low complexity, where both transmit antenna selection (TAS) and relay selection are deployed without eavesdropper channel state information (CSI). Both the exact and approximate secrecy outage probability (SOP) of proposed scheme are derived with outdated CSI under the cases of maximal ratio combining (MRC) scheme at the legitimate receiver. In the high signalto-noise ratio (SNR) regime, due to outdated CSI, the secrecy diversity order of MRC decreases to Nt+1 for relay selection and K + 1 for antenna selection. Finally, simulation results validate our analysis, and show that the proposed scheme alleviates the effect of the transmitter and relay links on SOP.

This study presents a new machine learning (support vector machine (SVM))‐based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non‐redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning‐based CSS model (proposed by Karaputugala et al. ) in terms of security, energy consumption, and sensing efficiency.

Bistable parallel array system can efficiently improve the signal‐to‐noise ratio (SNR) gain and further significantly lower bit‐error‐rate (BER) when the SNR is low. The bistable parallel array system is widely studied due to its outstanding advantages. This study presents a new method that applies combining techniques including maximum ratio combining (MRC), equal gain combining (EGC), and selection combining (SC) to the bistable parallel array system, and derives the formulas of the SNR gain, energy efficiency, and numerically calculates the BER. Through comparative analysis of these three combining techniques, it demonstrates the BER performance of the EGC technique in bistable parallel array system slightly inferiors to MRC, and the worst is SC. While the performance of all these three kinds of combining techniques is better than that of the single branch case. Importantly, higher SNR gain, higher EE and lower BER can be obtained in non‐Gaussian noise cases than that in Gaussian noise cases.

This study presents a novel message‐driven frequency‐hopping (MDFH) scheme leveraging slow frequency‐hopping (FH). Since the typical MDFH scheme requires a large amount of detectors to obtain the information that is transmitted through carrier frequency selection, the authors aim at a low‐cost MDFH implementation in this study. The basic idea of the proposed FH scheme is to transmit a part of information through the hop rate instead of the carrier frequency selection of an FH signal, which results in reduced hardware cost and better system efficiency. Moreover, a collision‐free multi‐carrier extension is also introduced in this study. The lower bound on the bit error rate (BER) performance of the proposed scheme is analysed and is verified by numerical results. The numerical results also reveal that the proposed scheme has a better BER and throughput performances than the typical MDFH systems at a low signal‐to‐noise ratio.

Hybrid interweave-underlay spectrum access in cognitive radio networks can explore spectrum opportunities when primary users (PUs) are either active or inactive, which significantly improves spectrum utilization. The practical wireless systems, such as long-term evolution-advanced, usually operate at multiple transmission power levels, leading to a multiple primary transmission power scenario. In such a case, the two fundamental issues in hybrid interweave-underlay spectrum access are to detect the “ON/OFF” status of PUs and to recognize the operating power level of PUs, which are challenging due to non-Gaussian transmitted signals. In this paper, we exploit high-order cumulants (HOCs) to efficiently perform spectrum sensing and power recognition. Specifically, for a given order and time lag, we first propose a single HOC-based spectrum sensing and power recognition scheme with low computational complexity, by leveraging minimum Bayes risk criterion. Moreover, we propose a hybrid multiple HOCs-based spectrum sensing and power recognition scheme with multiple orders and time lags, to further improve the detection performance. Both the proposed schemes can eliminate the adverse impact of the noise power uncertainty. Finally, simulation results are provided to evaluate the proposed schemes.

Hardware limitation at the secondary user (SU) terminal makes multiband (wideband) spectrum sensing more challenging. This paper considers spectrum sensing under SU hardware limitation, where the SU can only sense a small portion of the multiband spectrum for a given time period. This introduces a design issue of selecting channels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. Considering the Markov property of the state transition of a primary user (PU), an adaptive spectrum sensing strategy (ASSS) is then proposed to take advantage of the PU traffic patterns in determining the subchannels to sense. In the proposed ASSS, a novel decision rule is designed and two decision combinations are obtained. The ASSS decision rule adaptively selects a decision combination to determine the subchannels to sense for SU such that the selected subchannels are more likely to be available for the SU network. A metric called spectrum sensing capability (SSC) is defined to evaluate the performance of any spectrum sensing strategies. The SSC expressions for both RSSS and ASSS are derived. Numerical results show significant performance gain of ASSS as compared to RSSS.

In this paper, we develop the resource allocation scheme with heterogeneous statistical QoS provisioning for wireless powered sensor networks (WPSNs). In particular, we build up the downlink energy transfer and heterogeneous statistical QoS provisioning uplink data transmission models, where the aggregate effective capacity (AEC) is defined as the aggregate throughput under the statistical QoS constraints, for WPSNs. Based on the models, we formulate the AEC maximization problem to jointly optimize the downlink energy assignment and uplink power control scheme. To efficiently solve this problem, we divide it into the hybrid access point determined downlink energy assignment problem and the sensor node determined uplink power control problem, solving which yields the joint downlink energy assignment and uplink power control scheme. Extensive simulations are conducted to evaluate the performance of our proposed heterogeneous QoS-driven resource allocation scheme. The obtained results show that the heterogeneous QoSdriven resource allocation scheme can significantly increase the AEC as compared with the homogeneous QoS-driven resource allocation scheme.

In this paper, we propose a spectrum sensing and power classification scheme in hybrid interweave- underlay cognitive radio networks, considering that the primary system is with multiple transmission powers and the noise at the secondary user (SU) is spatially correlated. The primary target is to detect the presence of the primary user (PU), while the secondary target is to classify the transmission power of the PU, such that the SU can switch to underlay model with a flexible transmission power for fully exploring the spectrum access opportunities. The proposed scheme is a non-coherent detection scheme, where the weighted energy of the received signals serves as the decision metric. We derive the optimal sensing threshold for detecting the ``on/off" state of the PU as well as closed-form decision thresholds for classifying the PU's transmission power. The proposed scheme can efficiently identify the PU's transmission power by leveraging the correlation information of the noise observations. Simulation results are provided to evaluate the proposed scheme.

In this paper, we propose a high order cumulants based spectrum sensing and power recognition (CSR) detector for hybrid interweave-underlay spectrum access, where the primary system is with multiple transmit power levels. Specifically, to detect the idle spectrum when primary user (PU) is absent, high order cumulants based spectrum sensing is performed in interweave model. When PU is detected, the working model is switched to underlay model, where detection of PU's transmit power level is performed to allow secondary user (SU) to adjust its power for fully exploring spectrum access opportunities without harmful interference to PU. Given a certain order and a certain lag, the test statistics of the proposed detector is derived by leveraging general likelihood ratio test. Since cumulants higher than second order are zero for Gaussian distributions, the proposed CSR detector can extract non-Gaussian signal from Gaussian noise even when the noise is colored. Additionally, the proposed detector does not require any prior knowledge about the noise variance, thus it is robust to noise uncertainty. Closedform results for threshold expression are derived, and numerical results are provided to evaluate the proposed detector.

We focus on the 3-dimensional (3D) source localization passively by using TDOA and AOA in the presence of sensor errors. Determining the position from the TDOA and AOA measurements is not an easy task because the relationship between them is nonlinear. We present a new WLS solution that the TDOA equation is simpler than relevant literature (Yin and Wan, A Simple and Accurate TDOA-AOA Localization Method Using Two Stations). However, the bias of the WLS solution is larger. Hence, we propose a bias reduced method by imposing a quadratic constraint so that the expectation of cost function can attain the minimum value at the true position. The simulation illustrates the method is effective and the performance of this method can achieve the Cramer-Rao Lower Bound (CRLB) when the noises are Gaussian and in small region.

Energy harvesting gives a promising way to deal with the power constrained problem by harvesting energy from the external environment. In this paper, the multi-hop wireless network is considered where the relays with energy storage equipments are random scattered in several discs. The chosen relay nodes are used to decode-and-forward(DF) the received signals to the next terminal. The other active nodes in the discs harvest energy from the received signal. With the aim to achieve the minimum system outage probability, the close-form expressions are proposed about the optimal disc locations and about the optimal number of hops, respectively.

In this study, a cooperative wireless network is studied based on physical‐layer security, where multiple co‐channel interferers exist at relays, the destination and eavesdropper. Under the influence of multiple co‐channel interferers, the tradeoff between the security and reliability of wireless communications against eavesdropping attacks is analysed in terms of the intercept probability (IP) and the outage probability (OP). The exact closed‐form expressions for the IP and OP are derived for the direct transmission (DT) and the relay selection (RS) schemes over Rayleigh fading channels. The theoretical analysis is validated by numerical results. It is shown that the increase of the number of relays and (or) the number of co‐channel interferers can efficiently improve the security–reliability tradeoff (SRT). Moreover, the RS strategy can achieve a higher SRT performance compared with the DT strategy.

The performance of an underlay cognitive radio network equipped with multiple decode‐and‐forward relays is investigated in the presence of multiple primary transmitters and multiple primary receivers. The allowed maximum transmission powers for the secondary nodes are obtained according to the outage constraint of multiple primary users (PUs). The authors study selective cooperation scheme for the scenarios of non‐direct relay transmission, direct and relay transmission with selection combining, direct and relay transmission with maximum ratio combining, and incremental relay transmission. Considering the dependence among the received signal‐to‐interference‐plus‐noise ratios caused by multiple PUs, exact and asymptotical expressions for the outage probability of the proposed selective cooperation schemes are derived over Rayleigh fading channels under underlay spectrum sharing constraints. It is shown that the diversity order for the underlay cognitive relay network with multiple primary transmitters and multiple primary receivers is zero. Finally, simulation results are presented to verify the correctness of the authors’ theoretical derivations.

To boost the performance of binary pulse amplitude modulation at low signal‐to‐noise ratio, the parameter‐tuned stochastic resonance (SR) is introduced into digital communications system. In this study, an analytical framework is developed for evaluating the system performance by approximating the probability density function of the Ornstein–Uhlebeck noise based on the central limit theorem. Expression for the bit error rate of the bistable SR system with coherent receiver is derived. Theoretical and numerical results are presented to verify the analysis that the noise can improve the performance of the SR system with non‐coherent receiver. Also, it is shown that the performance of the bistable SR system with coherent receiver is superior to that with non‐coherent receiver, and the background noise is not favourable to signal processing in the coherent receiver.

This study considers the effect of channel estimation error (CEE) on the soft‐output bit log‐likelihood ratio (LLR) in a multiple‐input–multiple‐output detection system which the source sends their quadrature amplitude modulation to the detector through Rayleigh fading channels. The new expressions of LLR with maximum‐likelihood CE for deterministic channel model and minimum mean square error (MMSE) CE for random channel model are obtained separately for linear MMSE receiver using known pilot‐symbol‐aided CE. Meanwhile, the optimal power allocation between training and data transmissions for these two different LLR expressions, which attends to get the maximum ratio of signal and interference plus noise ratio, are obtained under the total transmitting power constraints. Numerical results show that the power allocation ratio sets to be ∼0.5 is optimal for two new different LLRs at the same total power and system noise. Moreover, the derived LLR expressions match the simulation result and outperform the conventional system without the consideration of CEE and optimal power allocation. Meanwhile, the system performance is better than the existing research with the consideration of CE.

Due to the broadcast nature of the radio propagation, requirements of high data rates and low probability of detection (LPD) form a well‐known trade‐off problem in covert wireless communication. Frequency hopping (FH) is a communication technology that is able to efficiently solve this problem by randomly switching its transmission channels. However, it is extremely complex to design an FH sequence (FHS) in a coloured noise scenario where different channels have different noise levels. To address this issue, in this paper, we propose a novel probabilistic FHS based on spectrum sensing. The FHS achieves the LPD among typical FHS under the constraint on a prescribed bit error rate. The authors present two algorithms that are used to generate the FHS and we analyse their computational complexity. To evaluate the performance of the sequence, we derive an expression for the probability of detection (PD). Simulation results show that the system bit error rate and the PD of the proposed FHS are low, and can be flexibly adjusted according to various practical requirements.

A carrier synchronisation technique for multiple symbol Trellis‐coded continuous phase frequency shift keying (MSTC‐CPFSK) system in burst‐mode transmission is proposed. For the synchronisation technique, a joint data‐aided (DA) acquisition and phase‐locked loop (PLL) tracking algorithm is presented. First, an all‐zero sequence is utilised as a pilot for DA acquisition, where the modulated pilot waveform is a direct current (DC) signal, eliminating the operation of modulation removal; and then, a second‐order PLL structure is introduced for tracking processing, and we make an analysis of the tracking performance by calculating the equivalent Cramer–Rao bound (ECRB) for the second‐order PLL structure; subsequently, we extend the obtained result to any order PLL structures; moreover, the ECRB for the MSTC‐CPFSK system is also derived through arithmetical calculation; finally, some numerical results are given to verify the performance of the authors presented synchronisation algorithm, and the simulations illustrate that their algorithm can improve the throughput compared with the classical DA‐only algorithm.

In this paper, we consider the sequential detection problem in a new cognitive radio scenario when the primary user (PU) works with more than one transmit power level. Different from most existing literature where PU is assumed to operate with a constant transmit power only, this new consideration well matches the practical standards, e.g., IEEE 802.11 Series, LTE, LTE-A, and so on, as well as the adaptive powering concept that a user would vary its transmit power under different situations. The targets of the secondary user here are not only to detect the presence of PU but also to recognize PU's transmit power levels. We first formulate a valid sequential detection approach via the modified Neyman-Pearson criterion and then derive the closed-form decision region for each PU's transmit power level. Many interesting discussions are raised. Moreover, the average sample number, a key metric for any sequential detection method, is derived in closed form to facilitate the performance evaluation. The performance comparison of the sequential detection and the fixed sample detection for this multiple primary transmit power levels scenario is analyzed. Finally, the simulation results are presented to verify the correctness of the proposed studies.

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes-Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.

In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers.

A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A 1-D search algorithm is proposed to solve these problems based on S-Procedure under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.

A max-min fairness resource allocation is studied for wideband cognitive radio under sensing-based spectrum sharing with simultaneous wireless information and power transfer. Specifically, the throughput of the worse-case secondary user is maximized by jointly optimizing the sensing time, transmit power and subchannel allocation, subject to constraints on energy harvesting, interference power and transmit power. The formulated max-min fairness resource allocation problem is a mixed integer non-convex programming. An efficient one-dimensional search algorithm based on the proposed transmit power and subchannel allocation scheme is designed to solve the formulated problem. Several tradeoffs are found, such as a tradeoff between the sensing performance and the throughput of the secondary user under a max-min fairness criterion.

In this paper, we consider the sequential spectrum sensing in a practice-matching cognitive raio (CR) scenario, where the primary user (PU) can operate under more than one transmit power levels as regulated in IEEE 802.11 Series, LTE, LTE-A, etc. In this case, the sensing targets not only include detecting the presence of PU but also include recognizing the transmit power level of PU. We propose two different sequential sensing schemes, i.e., detection before recognition and recognition before detection, and also derive the closed form decision regions. Performance of both schemes are compared. Numerical examples are then provided to corroborate the proposed studies.

In this paper, we propose two low-complexity automatic modulation classification (AMC) classifiers based on order-statistics: the linear support vector machine (LSVM) and the approximate maximum likelihood (AML). Specifically, LSVM applies the linear combination of the entire order-statistics of the received signals for the classification, while AML resorts to the asymptotic distribution of the reduced order- statistics to decrease the computational complexity. The Simulations show that the performance of our proposed classifiers is close to that of the maximum likelihood (ML) classifier and outperforms the Kolmogorov-Smirnov (KS) and cumulant-based classifiers. While the complexity of our proposed classifiers is much lower than that of the ML classifier.

Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue‐based cooperative spectrum sensing methods in some conditions, and it has lower complexity.

Spectrum sensing is performed at the beginning of each time slot in traditional cognitive radio networks, which is unreasonable and needless since the presence or the absence of a primary user (PU) always lasts several time slots. A hidden Markov model is used to describe the imperfect spectrum sensing process over Rayleigh fading channels. On the basis of the sensing results, a hybrid interweave/underlay mode is exploited by the secondary user (SU) to achieve a higher throughput. To solve the tradeoff problem among the average energy consumption for spectrum sensing, the average throughput of SU and the average interference to the PU, an optimisation problem is proposed. The optimal sensing interval to determine when the next spectrum sensing should be performed is obtained by solving the optimisation problem. Numerical results are given to verify the authors’ analysis.

In multiple‐input–multiple‐output broadcast channels, lattice reduction (LR) preprocessing technique can significantly improve the precoding performance. Among the existing LR algorithms, the fixed complexity Lenstra–Lenstra–Lovasz (fcLLL) algorithm applying limited number of LLL loops is suitable for the real‐time communication system. However, fcLLL algorithm suffers from higher average complexity. Aiming at this problem, a computationally efficient fcLLL (CE‐fcLLL) algorithm for LR‐aided (LRA) precoding is developed in this study. First, the authors analyse the impact of fcLLL algorithm on the signal‐to‐noise ratio performance of LRA precoding by a power factor (PF) which is defined to measure the relation of reduced basis and transmit power of LRA precoding. Then, they propose a CE‐fcLLL algorithm by designing a new LLL loop and introducing new early termination conditions to reduce redundant and inefficient LR operation in fcLLL algorithm. Finally, they define a PF loss factor to optimise the PF threshold and the number of LLL loops, which can lead to a performance‐complexity tradeoff. Simulation results show that the proposed algorithm for LRA precoding can achieve better bit‐error‐rate performance than the fcLLL algorithm with remarkable complexity savings in the same upper complexity bound.

A definition of least cyclostationary order (LCO) is proposed based on the inner cyclic period of single carrier and orthogonal frequency division multiplexing (OFDM) signals. The LCOs of single carrier and OFDM signals are derived. Since the LCO has a relationship with the carrier frequency offset (CFO), a blind CFO estimation scheme for single carrier and OFDM signals can be proposed. The novel scheme is entirely blind without any priori information and is proved to be the same as cumulants estimation when the LCO is known. However, the new scheme is superior to the estimation based on cumulant when the LCO is unknown. The mean‐square error of M ‐phase shift keying signals approaches the Cramér–Rao bound in additive white Gaussian noise (AWGN) channel. Simulation results are given to verify the effectiveness of the proposed blind estimation scheme in Rician fading channel and in AWGN channel.

Performance analysis is presented for the joint estimation of symbol timing, frequency offset, and phase offset with continuous‐phase modulation. In this study, undesirable influences on parameters to be estimated, which arise from the inaccuracy of the already estimated parameters, are computed mathematically. Based on performance analysis, a conditional Cramer‐Rao bound (CRB) is put forward, and a hypothesise is made that phase offset variance with frequency error present should match with the conditional CRB for any digital phase modulation. Moreover, the hypothesis is manifested by analysing the log‐likelihood function in a more general mathematical manner. To alleviate the influences, a data‐aided acquisition and phase‐locked loop tracking algorithm is proposed. The results of Monte Carlo simulations are presented, showing good agreement with theoretical analysis. Simulation results also show that the proposed algorithm can improve performance in terms of phase error variance at the expense of a high signal‐to‐noise ratio threshold.

An efficient cooperative spectrum sensing (SS) scheme based on Cholesky decomposition of the covariance matrix is proposed. The maximum eigenvalue (ME) of the matrix obtained by Cholesky decomposition is used as a test statistic. Analytical expressions for the false alarm probability and the decision threshold are derived. The effects of noise uncertainty on the ME SS algorithm and on the Cholesky ME cooperative SS scheme are assessed. It is proved that the proposed SS scheme is more robust than the conventional ME SS scheme in terms of noise uncertainty. Simulation results show that the performance of the proposed scheme is better than that of existing ME SS scheme in some conditions.

The estimation of the time differences of arrival (TDOAs) is significant in passive source localisation systems. The TDOA estimation accuracy may directly affect the source location performance. For co‐frequency interference environments, the authors address the problem of the passive blind estimation of time‐delays for uncorrelated interference source signals based on wireless sensor networks. The received mixtures at the sensors are modelled as unknown linear combinations of the differently delayed versions of the communication signal and the interference signal. Blind source separation and secondary interference signal extracting are both introduced in the proposed method. The interference signals in the mixed receiving signals of all the sensors are extracted effectively and the effect of the mixed communication signals can be significantly reduced. Simulations show that the proposed method has a more accurate performance compared to other TDOA estimation methods, and is therefore valid and practical in the TDOA localisation systems.

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

The performance of underlay cognitive relay networks with multiple primary users and multiple proactive decode-and-forward relays is investigated. Particularly, both interference power constraints at multiple primary receivers and multiple primary transmitters' interference at secondary transmission nodes are taken into account. Considering correlations among the received signal-to- interference-plus-noise ratios, exact closed-form expressions for the outage probabilities of secondary users are derived for the cases with or without a direct secondary link over Rayleigh fading channels. A novel method is proposed to analyze the outage probability for cognitive proactive decode- and-forward relay networks in the presence of a direct secondary link. Simulation results show that the diversity gain is zero in the presence of multiple primary transmitters' interference, but the outage floor in the high SNR can be significantly decreased by an increase of the number of relays or by exploiting a direct secondary link.

For meteor burst communications (MBC), although per-survivor processing (PSP) based on joint data and channel estimation provides superior performance and robustness for meteor burst communications (MBC), great computational complexity constrains its application in practice. On this occasion, a dimension-down PSP (D- PSP) algorithm and a adaptive state reduction of PSP (ASRP) algorithm are proposed to curve this problem in this paper. On the basis of this, the adaptive state reduction using a dimension-down PSP (ADPSP) is proposed to combine the advantages of D-PSP and ASRP, which reduces the computational time and memory size for exponentially decaying meteor burst channels and makes maximum likelihood sequence detection (MLSD) possible for adaptive data transmission. Simulation results are presented to validate the theoretical analysis. It is shown that deploying the proposed ADPSP algorithm can achieve a good tradeoff between performance and computational complexity dynamically, and provide reliable data transmission for MBC systems with adaptive coding and modulation.

The outage behavior of a dual-hop multi-source multi-relay cooperative underlay cognitive radio network is investigated in the presence of multiple primary transmitters and multiple primary receivers. Firstly, with primary users' (PUs') outage constraints, an adaptive power control scheme is adopted to obtain maximum allowed secondary transmission power. Then, an SINR-based scheme is proposed for the combined use of cooperative diversity and multiuser diversity, where the best secondary source-relay pair is selected to communicate with the secondary destination. In this context, exact closed-form expressions for the outage probability of reactive decode-and-forward (DF) relaying are derived for maximum ratio combining (MRC) and selection combining (SC) over Rayleigh fading channels, respectively. In particular, we consider the dependence among the received signal-to-interference-plus-noise ratios (SINRs) at the destination caused by multiple PUs. It is shown that although there exists outage floor in the high signal-to-noise ratio (SNR) regime, increasing the number of secondary sources or available relays are efficient ways to decrease the outage floor.

The exponential growth of wireless communications has resulted in tremendous energy consumption and significant environmental pollution. The energy efficiency (EE) of the wireless system has to be improved in order to achieve green wireless communications. Against this background, we provide a wireless EE improvement scheme based on nonlinear stochastic resonance (SR) technique. Firstly, we explore the mechanism of the bistable SR system and build the normalized ideal reference SR (IRSR) model. On this basis, we further get the analytical expression of bistable system parameters, which make the realization of the SR system be more easy. Finally, we introduce the SR system as the preprocessor of the wireless system receiver, in which one part of noise energy can be utilized to enhance the signal transmission. Theoretical analyses and simulation results show the effectiveness of our proposed scheme in improving the EE of the wireless system.

In this study, the authors investigate the impact of the frequency‐hopping (FH) sequence on the packet transmission performance of the asynchronous frequency‐hopping multiple‐access (FHMA) network. According to the model of the asynchronous FHMA network given in this study, the authors analyse the impact of the two important FH sequence factors – uniformity and frequency slot number – on the correct transmission probability and the packet transmission frequency efficiency. According to the non‐collision probability analysed in this study, the uniformity of the FH sequence is an important issue to determine the correct packet transmission probability. Moreover, based on the definition of the packet transmission frequency efficiency given in this study, the optimal frequency slot number employed to obtain the maximum frequency efficiency is obtained. By employing three typical FH sequences, the simulation results are also included, which validates the theoretical analysis about the packet transmission performance of the asynchronous FHMA network.

In this study, the authors investigate the effect of imperfect channel state information (CSI) on underlay cognitive radio networks in the presence of multiple primary users (PUs). Particularly, with multiple proactive partial decode‐and‐forward (DF) relays available, both the relay selection channels and interference channels are assumed to be imperfect. In this context, firstly, based on the instantaneous CSI (ICSI) of interference channels, exact closed form expressions for the outage probability (OP) and the interference probability (IP) are derived, where IP is used as a performance metric to quantify the impact of harmful interference caused by secondary user (SU) on PUs. It is shown that, when the ICSI of interference channels are imperfect, the secondary networks always interfere with PUs' communications. Then, to reduce the harmful interference caused by secondary nodes, we propose an adaptive power control scheme relying on the partial CSI (PCSI) of interference channels. Finally, simulation results verify the accuracy and effectiveness of the author's analytical study, and indicate the proposed scheme strikes a good tradeoff between the interference introduced by secondary nodes on PUs and SU's performance.

Energy detection is the most frequently used spectrum sensing technique, however, in uncertain low signal‐to‐noise ratio (SNR) conditions, it generally suffers from the ‘SNR wall’, that is, a minimum SNR below which it is impossible to reliably detect a signal. To address this issue, an improved energy detection (IED) algorithm based on non‐linear parameter‐induced stochastic resonator (PSR) is proposed in this study. By adopting the method which combines classic adiabatic approximation stochastic resonance (SR) theory and non‐classic parameter‐induced SR theory, an analytical expression of SR system parameters is derived. On this basis, a PSR is proposed which is mathematically testified to have the ability to improve the SNR of the received signal. Further, an IED algorithm is proposed by introducing the PSR as the preprocessor of energy detection. Theoretical analyses and simulation results prove that a significantly detection performance and SNR wall improvement can be achieved using the proposed IED algorithm.

Spectrum sensing has been well studied because of its significance in cognitive radio. Different from the existing works which a primary user (PU) is assumed to have only one constant transmit power, a more practical scenario that the PU transmitting with multiple power levels is considered. A continuous hidden Markov model (CHMM)‐based blind algorithm for not only detecting the presence of PU but also recognising the transmit power level of the PU is proposed. The training problem of CHMM is solved by combining the wavelet singularity detection with k ‐means clustering algorithm. An effective method for estimation of the number of power levels is proposed. Two different strategies are designed to perform spectrum sensing. Simulation results show the efficiency of the proposed algorithm.

The opportunistic spectrum access technology is one of the most promising methods for alleviating the spectrum scarcity problem, which enables a secondary user (SU) to utilise a primary user spectrum band that is detected idle. However, the throughput achieved by cognitive radios is limited by the interference constraint imposing on the SU. Multiple input single output antenna techniques and antenna selection (AS) techniques are exploited to combat the interference constraint and improve the achievable average throughput of the SU. The optimal power allocation strategy is proposed to maximise the achievable average throughput. Performance analyses for the achievable average throughputs are performed under the maximum channel gain AS strategy, the minimum interference channel gain AS strategy and the ratio AS strategy. It is proved that the optimal transmitted AS strategy is the ratio AS strategy when the optimal power allocation strategy is used. The optimal sensing parameters are designed to further improve the maximum average throughput. Extensive simulation results are conducted to verify this analysis.

The enhancement of noisy images has been playing a key role in improving the visual effect and the performance of image processing. Traditional methods for image enhancement are mainly focusing on eliminating noise, which cannot acquire good effect under low peak‐signal‐to‐noise ratio (PSNR) conditions. Stochastic resonance (SR), on the contrary, is a technique using noise to enhance signal. Owing to the unique feature of SR, a novel binary image enhancement scheme based on aperiodic SR (ASR) technique is proposed. In this study, the authors take the improvement in PSNR as a measure of the ASR‐based binary image enhancement system, which provides a guideline for the realisation of the ASR system. On this basis, they obtain the PSNR expression of the ASR‐based binary image enhancement system. Simulation results show that the proposed method is superior to the traditional binary image enhancement methods both in visual effect and PSNR performance.

In wireless communication, the amplitude and phase of the transmitted signal have certain patterns during one symbol duration, which introduces high correlation among the samples obtained by oversampling at the receiver. In this work, we aim to explore such correlation information for cognitive radios to enhance the performance of spectrum sensing. By jointly considering the signal modulation, multipath fading, and oversampling rate, we derive the distribution of the empirical autocorrelation function for the obtained samples, on which we propose two efficient spectrum-sensing algorithms, and then analyze their performance. Our theoretical results reveal that the proposed algorithms with oversampling perform strictly better than the conventional energy detection scheme, while requiring the same level of prior information. Finally, we show through simulations that the derived statistical characteristics approximate the true statistical distribution of the autocorrelation function well, and the proposed sensing algorithms significantly improve the sensing performance compared to several existing sensing schemes.

Spectrum sensing is the precondition of implementation of cognitive radio. Motivated by the fact that eigenvalue detection algorithms are based on eigenvalue decomposition over the covariance matrix, we propose an efficient spectrum sensing algorithm based on Cholesky decomposition over that matrix. Using eigenvalues of the matrix which is obtained by Cholesky decomposition over finite covariance matrix, the efficient spectrum sensing algorithm is proposed. Attractive advantages of our proposed technique are: a) no assumptions on the sampling size and the dimension of the random matrix are required; b) exact and simple closed-form analytical expressions for the false alarm probability and decision threshold are derived under practical scenarios of finite size of the covariance matrix and samples; c) numerical simulations show that the presented algorithm achieves performance improvement compared with previous algorithms based on eigenvalue.

As spectrum sensing detects the presence of primary user (PU) signal, an efficient and reliable spectrum sensing scheme plays a critical role in cognitive radio (CR). In this paper, a novel spectrum sensing method via stochastic resonance (SR) under low signal-to-noise ratio (SNR) is presented. It is shown that the introducing of suitable additional noise can enhance sequential energy detector (SED). Theoretical analysis and the optimal SR noise probability distribution function (PDF) are given. Simulation results show that in comparison with traditional sequential energy detection, our method delivers considerable reduction on the average sample number (ASN), while maintaining a comparable detection performance under low SNR.

Traditional symbol rate estimators based on wavelet transform (WT) suffer from difficulties in choosing wavelet scale. Additionally, the most estimators are only adapted to limited signal types. Furthermore, those estimators are susceptible to noise, which is impractical to implement. In this paper, to overcome shortages of those traditional estimators, a self- adapting symbol rate estimator is proposed. The proposed estimator has advantages in reliably working under low Eb-to-N0. More importantly, it can be widely used to estimate various types of signal. The numerical simulations show that the proposed estimator has superiority in performance compared with the improved estimators based on wavelet transform.

The accuracy of sources locations and velocities estimate is very sensitive to the accurate knowledge of sensor locations and velocities. In the presence of sensor position and velocity errors, this study considers the problem of simultaneously locating multiple disjoint sources and refining erroneous sensor positions and velocities using time differences of arrival and frequency differences of arrival. The previous work by Sun and Ho to solve this problem provided an efficient estimator for multiple disjoint sources, but it cannot provide optimum accuracy for the sensor positions and sensor velocities. In many practical applications, it is necessary and helpful to refine sensor locations and velocities while localising multiple sources. The proposed method improves the previous method so that both the source and the sensor position and velocity estimates can achieve the Cramér–Rao lower bound accuracy very well over small noise region. The theoretical derivation is corroborated by simulations.

In practice, sensing results often affect the data transmission in cognitive radio systems. Therefore, a hybrid interweave/underlay cognitive radio system is presented by jointly considering spectrum sensing and data transmission. In particular, multiple relays are deployed for both the spectrum sensing and data transmission. The secondary user (SU) source and the relays cooperatively sense the spectrum with energy detection and decision fusion. According to the cooperative spectrum sensing results, the SU adaptively switches between interweave and underlay spectrum access mode. Moreover, to improve the spectral efficiency, an adaptive modulation technique is integrated into SU's transmission. In this context, undertaking the interference from the primary user (PU), the upper bound for average spectral efficiency of the SU with continuous-rate adaptive modulation is derived over Rayleigh fading channels. Further, we study the performance of SU, where adaptive L-ary quadrature amplitude modulation (L-QAM) with fixed switching threshold is adopted. Simulation results verify our analysis and show that both hybrid interweave/underlay scheme and adaptive modulation can improve the SU's performance significantly.

As spectrum sensing detects the presence of PU signal, an efficient and reliable spectrum sensing scheme plays a critical role in CR. For this purpose, sequential sensing technique is introduced to reduce the sensing time to the minimum while desirable detection performance is maintained. However the sensing time could still be unacceptably long due to the weak PU signal, especially in non-Gaussian noise. To improve spectrum sensing efficiency, we propose a novel sequential sensing scheme based on suprathreshold stochastic resonance (SSR). We address the theoretical bound to achieve potential performance improvement and give the applicable algorithm of SSR-based sequential sensing scheme. In the scheme, the average sample number (ASN) is reduced in a single sensing node using nonlinear stochastic resonance method. The simulation results show that the proposed scheme significantly outperforms the conventional scheme, especially in low signal-to-noise ratio (SNR) scenario.

Making new social interactions with other users in vicinity is a crucial service in Proximity-based Mobile Social Networks (PMSNs), where a user can find a best matching friend directly through the Bluetooth/WiFi interfaces built in her mobile device. In existing work for such services, users have to publish their interests to do the matching. However, it conflicts with users' growing privacy concerns about revealing their interests to strangers. To tackle this problem, we propose Weighted Average Similarity (WAS) algorithm, which considers both the number of common interests and the corresponding weights on them, to protect users' privacy without reliance on any Trusted Third Party (TTP). Users set their interests into several priority levels with different weights, then WAS can provide a high level similarity value among these participants without revealing any information about their common interests. The security and computation/communication overhead of our scheme are thoroughly analyzed and evaluated via detailed simulations.

Location-Based Services (LBSs) are becoming increasingly popular in our daily life. In some scenarios, multiple users may seek data of same interest from a LBS server simultaneously or one by one, and they may need to provide their exact locations to the un-trusted LBS server in order to enjoy such a location-based service. Unfortunately, this will breach users' location privacy and security. To address this problem, we propose a novel collaborative system, MobiCache, which combines k-anonymity with caching together to protect user's location privacy while improving the cache hit ratio. Different from the traditional k-anonymity, our Dummy Selection Algorithm (DSA) chooses dummy locations which have not been queried before to increase the cache hit ratio. We also propose an enhanced-DSA to further improve the user's privacy as well as the cache hit ratio by assigning dummy locations which can make more contributions to cache hit ratio. Evaluation results show that the proposed DSA can increase the cache hit ratio and the enhanced-DSA can further improve the cache hit ratio as well as the user's privacy.

In the presence of primary user (PU), most of researches have often neglected the effect of the interference from the primary transmitter to secondary receiver in cognitive radio (CR) systems. In this paper, with the PU's interference, we investigate the problem of optimal joint relay selection (RS) and power allocation (PA) to achieve maximum throughput in spectrum sharing cognitive two-way relaying networks. A closed-form solution is proposed for optimal allocation of transmit power among the secondary user (SU) transceivers and the SU relay. Simulation results show the PU's interference degrades the effectiveness of the system. Moreover, our proposed power allocation and relay selection methods can achieve maximum throughput in the presence of PU's interference.

Passive source localization has been the focus of considerable research efforts due to its usefulness in various applications. This paper performs a fundamental investigation of whether the gain ratios of arrival (GROAs) can be utilized in conjunction with the time differences of arrival (TDOAs) to improve the multiple sources localization accuracy with erroneous sensor positions. It's an urgent need to find a closed-form solution with good localization accuracy for this challenging problem. This paper takes the advantage that the TDOAs and GROAs from different sources have the same sensor position displacements and proposes an estimator that jointly estimates the unknown sources and sensor positions. In order to establish an closed-form solution for multiple sources location estimates using GROAs and TDOAs, we use the idea of hypothesized source locations in the algorithm development to enable the formulation of pseudo-linear equations. The proposed algebraic solution does not require initialization and does not have divergence problem. Our conclusion is that the improvement from GROAs is obvious for multiple sources localization compared with methods only using TDOAs. Numerical simulations are included to support and corroborate the theoretical developments.

To tackle the problem of performance degradation of traditional spectrum sensing technique with energy detection under the circumstances of weak signal and non-Gaussian environment in cognitive radio (CR), a novel spectrum sensing method based on suprathreshold stochastic resonance (SSR) is proposed in this paper. Through the resonance between the primary user (PU) signal and noise by introducing nonlinearity of a parallel of quantizers, the signal-to-noise ratio (SNR) of the received signal can be increased when the constraint we develop is satisfied. To obtain a constant false-alarm rate (CFAR), the detection probability of the proposed method is derived. Theoretical analyses and simulation results show that the detection performance is superior to the conventional energy detection method under low SNR circumstances when the a certain range of non-Gaussian noise is input.

In this paper, with multiple relays available for cooperative sensing and data transmission, we investigate the performance of primary user (PU) and secondary user (SU) in the hybrid interweave and underlay cognitive relay networks. For the cooperative sensing phase, all the relays independently sense the spectrum with energy detection, and hard fusion is deployed to achieve the final sensing result by a distributed manner. Subsequently, for the data transmission phase, when cooperative sensing results assume there exists a spectrum hole, the SU transmitter and the selected relay transmits the message with maximum transmission power. Otherwise, to guarantee the PU's transmission quality, the SU transmits the message under a peak power constraint at the PU receiver. It is noted that the selected relay can decode the SU's message correctly and has the largest signal to noise ratio (SNR) or signal to interference and noise ratio (SINR) at the SU receiver. In this case, considering the interference caused by the PU, we derive the outage probability of SU under Rayleigh fading channels. In addition, the impact of cooperative sensing on the PU are discussed. Finally, simulation results verify our analysis.

Passive source localization has been the focus of considerable research efforts due to its usefulness in various applications. This paper performs a fundamental investigation of whether the gain ratios of arrival (GROAs) can be utilized in conjunction with the range differences of arrival (RDOAs) to improve the multiple sources localization accuracy in the presence of sensor location uncertainties. In this paper, we derive the Cramer-Rao lower bound (CRLB) of multiple source location estimate using both RDOAs and GROAs when sensor positions have errors. Simulations show that the localization accuracy improvements contributed by GROA measurements are significant for two far-field sources, two near-field sources and two enclosed sources as the SNR, jamming signal bandwidth factor C/ωoor sensor position error power σs2increases. The CRLB will provide reasonable reference for localization algorithm research in the future.

In this paper, we investigate the performance of adaptive modulation in spectrum sharing cognitive relay networks, where the secondary user (SU) transmits the message by one best relay with amplify and forward (AF) relaying scheme. Moreover, to guarantee the primary user (PU)'s transmission quality, the interferences caused by the SU transmitter and the selected relay are less than a predetermined interference threshold. The capacity of adaptive modulation with opportunistic AF relaying is firstly derived under independent Rayleigh fading channels. Then, with adaptive L-QAM modulation and fixed switching threshold, the outage probability, average spectral efficiency, and the average error bit rate (BER) of the SU are derived. Finally, simulation results validate our analysis and show that adaptive modulation can improve the SU's performance significantly.

High complexity of frequency-hopping (FH)/spread- spectrum(SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the Fuzzy Entropy(FuzzyEn) is presented. Simulation and analytical results show that, the proposed FuzzyEn works can effectively discern the changing complexities of the FH/SS sequences, and are compared with complexity metric based on the Approximate Entropy(ApEn). The FuzzyEn scheme has obvious advantages in the robustness to resolution parameter, the dependence to observation length and the sensitivity to vector dimension.

In this paper, a methodology based on the cost of bus throughput, transmission latency and power consumption is proposed to achieve integrated optimization of NoC topology modeling and generation. The optimized framework's static and dynamic properties ensure efficient core-to-core communication of the complete network. Our approach 1) fully exploits the regularity of standard topology and the flexibility of application-specific topology 2) generates a scalable network containing heterogeneous topologies, which raises the abstraction level of reuse from resources to subnets 3) satisfies NoC application. The performances of the topology are simulated and compared between the proposed methodology and regular NoC topologies, including mesh and optimal mesh architecture. Experimental results show the proposed design flow is efficient to provide better performance for predefined requirements, and minimal cost function is achieved. In addition, we applied several multimedia applications as case studies.

Shrinking transistor sizes and recent trends toward many-core chips have heightened the need for an efficient on-chip communication network to integrate various cores. However, buses and point-to-point interconnection will not result in scalability, modularity, and explicit parallelism, as well as may suffer great performance bottleneck. While state-of-art packet-switched network increases the communication costs and is incapable of performing multicast service. In addition, the emergence of reconfigurable system needs a flexible and application-specific architecture which could dynamically customize the systems. HCR-NoC (Hybrid-Communication Reconfigurable Network-on-Chip) is proposed in this paper, which could dynamically reconfigure MPSoC architecture based on buses traffic. To satisfy different communication services, we use a TDMA shared bus for inter-cluster communication in a designable framework, which enables topology reconfiguration upon regular physical network topology. An analytical verification tool is presented to simulate HCR-NoC system performance. Finally, the evaluations of HCR-NoC using Multimedia benchmarks show that significant reduction in power and area as compared to optimal mesh architecture NoC.

In cooperative wireless networks, relay selection plays a significant role in the system performance. In this paper, with the presence of interferences, we firstly propose two relay selection protocols according to the forms of the interferences. Then considering that the interference caused by the selected relay has an effect on other transmissions, a threshold based relay selection protocol is proposed to select one best relay forwarding the message, where the interference caused by the selected relay is below than a predetermined threshold. Furthermore, in the proposed protocols, all the relays and the destination are subject to the interferences from other transmissions. The exact outage probabilities for the protocols are derived over the Rayleigh fading channels. Finally, simulation results validate our analysis,and show that our proposed relay selection protocols are better than the available relay selection protocols in terms of outage probability.

According to the demand of the Doppler frequency shift estimation under low signal-noise-ratio (SNR) conditions, the stochastic resonance (SR) technique from the nonlinear science is applied to signal pre-processing and a novel algorithm with the level crossing rate (LCR) estimation is proposed in this paper. Theoretical derivation demonstrates the proposed algorithm can effectively improve SNR and bandwidth ratio of the received signal, thereby reducing estimated error of the LCR algorithm. Moreover, after analysis and discussion of the optimal sampling frequency in stochastic resonance pre-processing (SRP), the best relationship of the sampling frequency with input SNR is obtained. The Monte-Carlo simulation results show that comparing with current algorithms, the proposed algorithm improves estimated performance of 2~4dB without increasing computational complexity under low SNRs, it verifies the consistency with the theoretical conclusions.

In this letter, as multiple interferers have an effect on both the relays and the destination, the exact outage probabilities for the decode-and-forward (DF) opportunistic relaying are derived in three different cases: a) with selection combining (SC) deployed at the destination, b) with maximal ratio combining (MRC) deployed at the destination, and c) with hybrid selection and maximal ratio combining (HSMC) deployed at the destination. Simulation results validate our analysis, and show that in terms of outage probability, opportunistic relaying with HSMC is much better than that with SC or MRC.

High complexity of frequency-hopping (FH)/spread-spectrum (SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. With the growing development in the design of FH/SS sequence in much wider fields, the well-known complexity measures - the linear complexity (LC), the linear complexity profile (LCP) and the k-error linear complexity (k-error LC) - are widely used but not sufficient to evaluate the complexities of the sequences available, such as the cryptographical sequence and the chaotic sequence families. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the approximate entropy (ApEn) is proposed in the view of the maximal randomness of the sequences with arbitrary length. And the theoretical bounds of the ApEn are derived from a probabilistic point of view. Simulations and analysis results show that, the proposed ApEn works effectively to discern the changing complexities of the FH/SS sequences with small number of samples, which provide superior performance over its candidates.

Minimizing the total transmission power is one of the main objectives of efficient broadcast algorithms in wireless networks, where all nodes are powered by battery with limited energy supply. Cooperative transmission is an important way to make full use of the space diversity and can save the transmission power significantly, especially in the wireless broadcast transmission, where the nodes can accumulate the overheard information. In this paper, firstly a centralized cooperative broadcast algorithm, which permits multiple nodes to cooperate in transmitting the broadcast message, is proposed to save the total transmission power. Considering that the centralized algorithm requiring a global knowledge of the networks is impractical in large wireless networks, the distributed version of the centralized algorithm requiring only 1-hop neighborhood information is proposed under the assumption that limited frequency band is available. The distributed algorithm combines the physical (PHY) and medium-access-control (MAC) layer mechanisms to reach all the nodes in a cooperative way. Though the proposed distributed algorithm is inferior to the centralized algorithm in terms of total transmission power, it takes less resource to broadcast than the centralized algorithms. Simulation results show that both the centralized algorithm and distributed algorithm can get a better performance than the existing broadcast algorithms. Furthermore, the distributed algorithm can achieve the total transmission power close to the centralized algorithm.

High complexity of frequency-hopping (FH)/spread-spectrum (SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. With the growing development in the design of FH/SS sequence in much wider fields, the well-known complexity measures-the linear complexity (LC), the linear complexity profile (LCP) and the k-error linear complexity (k-error LC)-are widely used but not sufficient to evaluate the complexities of the sequences available, such as the cryptographical sequence and the chaotic sequence families. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the approximate entropy (ApEn) is proposed in the view of the maximal randomness of the sequences with arbitrary length. And the theoretical bounds of the ApEn are derived from a probabilistic point of view. Simulations and analysis results show that, the proposed ApEn works effectively to discern the changing complexities of the FH/SS sequences with small number of samples, which provide superior performance over its candidates.

According to the characteristics of meteor burst channels, variable rate data transmission is often employed to improve the average system throughput, and the time-varying meteor channel in the presence of multi-path rays brings difficulties in both data detection and channel estimation at the receiver. As we know, per-survivor processing (PSP) based on the joint data and channel estimation of maximum likelihood sequence detection (MLSD) is often considered to obtain the optimal detection performance for time-varying channels. However, the great computational complexity of PSP is the major problem in practice. In this paper we propose an adaptive state reduction of PSP (ASRP) algorithm with few states in the trellis diagram for exponential decay of meteor channels. Based on the estimation of the meteor channel parameters, the adaptive threshold is derived to select the states close to the right one in the trellis to reduce the complexity of PSP. Theoretical analysis and computer simulations results show that the ASRP we proposed provides suboptimal performance and makes good tradeoff between the performance and the computational complexity, so that the reliable data transmission can be realized for MBC systems with adaptive modulation and coding (AMC).

High complexity of frequency-hopping (FH)/spreadspectrum (SS) sequence is of great importance to high-security multiple-access communication systems, for it makes FH/SS sequence difficult to be analyzed. With the growing development in the design of FH/SS sequence in much wider fields, the wellknown complexity measuresiquestthe linear complexity (LC), the linear complexity profile (LCP) and the k-error linear complexity (k-error LC)iquestare widely used but not sufficient to evaluate the complexities of the sequences available, such as the cryptographical sequence and the chaotic sequence families. In this paper, a new complexity metric to evaluate the unpredictability of FH/SS sequence based on the approximate entropy (ApEn) is proposed in the view of the maximal randomness of the sequences with arbitrary length. And the theoretical bounds of the ApEn are derived from a probabilistic point of view. Simulations and analysis results show that, the proposed ApEn works effectively to discern the changing complexities of the FH/SS sequences with small number of samples, which provide superior performance over its candidates.

Spatial subchannels in multiuser multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/OFDM) systems are invested. With the goal of maximizing the overall system throughput, we derive the criterion of subcarrier allocation and introduce an adaptive resource allocation algorithm. Furthermore, a time-frequency blockwise loading design which may decrease the computational complexity is suggested. The simulation results show that the algorithm can achieve good performance and high transmission rate efficiently.

According to the analysis of MBC (meteor burst communication) mechanism, a model of signal processing based on the structure of data frame is suggested for adaptive modulation and coding (AMC) of MBC system in this paper. There are two distinct modes of operation for signal processing: acquisition and tracking. The acquisition mode is a training period to initialize the channel estimation by frame header. The tracking mode is jointly to equalize payload data and to trace channel, where the principle of per-survivor processing (PSP) for maximum likelihood sequence detection (MLSD) is performed. A suboptimal method called D-PSP is adopted to save the computational time and memory size, which agrees with the slow-fading characteristic of meteor channel and makes the MLSD possible for adaptive modulation and coding of MBC system. Computer simulation results are included to support our development

In mobile OFDM communication systems, Doppler spread leads to the loss of orthogonality between the sub-carriers, limiting the achievable throughput of the moving transceivers. In this paper, the effect of Doppler spread in Rayleigh fading channels is investigated, and a method of time-variant Doppler frequency estimation and compensation is proposed for mobile OFDM systems. Computer simulation results are included to support our developments

A novel family of frequency hopping sequences based on 3DES iterated block cipher is proposed for frequency-hopping multiple-access (FHMA) communications. The design offers a class of nonlinear FH codes with high security, large linear span and a uniform spread over the entire frequency bandwidth. Moreover, FH sequences among the family are independent from each other and they perform as well as random patterns in terms of multiple access interference in anti-jamming applications. With the performance of packet error and throughput for FHMA network being derived in theory, many numerical results of the 3DES sequences are presented, comparing with those of shift register sequences and chaotic FH sequences. Efficiently implemented in field-programmable–gate-arrays (FPGA), the generator prototype of the proposed sequence has been realized and incorporated into fast FH radio.