With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ...In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.展开更多
With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of...With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of 5G/6G networks will inevitably lead to severe interference,resulting in degradation in the communication performance of maritime users.In this paper,we propose a safe deep reinforcement learning based interference coordination scheme to jointly optimize the power control and bandwidth allocation in maritime communication systems,and exploit the quality-of-service requirements of users as the risk value references to evaluate the communication policies.In particular,this scheme designs a deep neural network to select the communication policies through the evaluation network and update the parameters using the target network,which improves the communication performance and speeds up the convergence rate.Moreover,the Nash equilibrium of the interference coordination game and the computational complexity of the proposed scheme are analyzed.Simulation and experimental results verify the performance gain of the proposed scheme compared with benchmarks.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of l...Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of line-of-sight links and the broadcast nature of air-ground UAV communications make it vulnerable and prone to eavesdropping by malicious nodes.展开更多
Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because ...Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.展开更多
In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenario...In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.展开更多
To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase n...To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase noise and considering the correlation of neighboring symbols in the NLC section of DBP.Based on this model,with the aid of neural network optimization,a learned version of M-W-DBP(M-W-LDBP)is also proposed and explored.Furthermore,enough technical details are revealed for the first time,including the principle of our proposed M-W-DBP and M-W-LDBP,the training process,and the complexity analysis of different DBPclass NLC algorithms.Evaluated numerically with QPSK,16QAM,and PS-64QAM modulation formats,1-step-per-span(1-StPS)M-W-DBP/LDBP achieves up to 1.29/1.49 dB and 0.63/0.74 dB signal-to-noise ratio improvement compared to chromatic dispersion compensation(CDC)in 90-GBaud and 128-GBaud 1000-km single-channel transmission systems,respectively.Moreover,1-StPS M-W-DBP/LDBP provides a more powerful NLC ability than 2-StPS LDBP but only needs about 60%of the complexity.The effectiveness of the proposed M-W-DBP and M-W-LDBP in the presence of laser phase noise is also verified and the necessity of using the learned version of M-WDBP is also discussed.This work is a comprehensive study of M-W-DBP/LDBP and other DBP-class NLC algorithms in HSR systems.展开更多
The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defendin...The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.展开更多
The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional au...The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel.These vulnerabilities include brute-force attacks,single point of failure,and information leakage.Blockchain,as a decentralized distributed ledger with blockchain storage,tamper-proof,secure,and trustworthy features,can solve problems such as single-point-of-failure and trust issues,while the hidden communication in the physical layer can effectively resist information leakage and violent attacks.In this paper,we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication,where the identity information is transmitted as covert tags carried by normal modulated signals.In addition,a weight-based Practical Byzantine Fault-Tolerant(wPBFT)consensus protocol is devised,where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios.Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness,particularly under conditions of low Signal-to-Noise Ratio(SNR)and short tag length.展开更多
Unmanned aerial vehicles(UAVs),characterized by their low cost and operational flexibility,have been increasingly deployed across civilian,military,and commercial applications.To improve the coverage and connectivity,...Unmanned aerial vehicles(UAVs),characterized by their low cost and operational flexibility,have been increasingly deployed across civilian,military,and commercial applications.To improve the coverage and connectivity,UAVs can be utilized to realize the comprehensive spatial coverage for the sixth-generation mobile networks.However,the private data in UAV networks is easy to be exposed due to the light-of-sight links and openness of wireless transmission.Covert communication as an emerging technique has shown its superiority in hiding the transmission behavior,which can further enhance the security of UAV networks compared with the traditional physical-layer security.Therefore,in this article,we present a survey on the recent advanced research about covert UAV communications.First,the roles of UAVs for covert communications are described.Then,the covert UAV communications with different uncertainties are introduced.Moreover,the wireless techniques for covert UAV communications are explored.In addition,we point out the applications in covert UAV communications.Finally,the open research issues concerning practical scenarios and promising applications are highlighted.展开更多
Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often li...Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often limited by the“multiplicative fading”effect through reflection links,which becomes severe in the case of double reflections and significantly degrades the security performance.In this paper,we consider a wireless system that consists of a fixed passive RIS and an Unmanned Aerial Vehicle(UAV)-mounted active RIS,where the UAV-enabled aerial amplification and reflection are exploited to compensate for the multiplicative fading effect.We formulate the problem to maximize the secrecy rate by jointly considering the optimal deployment of the UAV-based active RIS and the reflection coefficients at both the passive and active RISs.To enable efficient algorithm design,we decompose the problem into two layers:the outer layer optimizes the UAV deployment through deep reinforcement learning,while the inner layer solves the beamforming and reflection design using a block coordinate descent framework.Simulation results demonstrate the convergence of the proposed learning procedure,and indicate that the active RIS with learned deployment can effectively enhance the reflection and significantly improve the secrecy rate.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedbac...In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.展开更多
The 3-D beamforming scheme has elite as evolving interest because of its efficiency to empower assorted techniques such as vertical and horizontal domains and emanation beamforming according to subscriber's provis...The 3-D beamforming scheme has elite as evolving interest because of its efficiency to empower assorted techniques such as vertical and horizontal domains and emanation beamforming according to subscriber's provisions. Usually, 3-D beamforming communication is set up on FDD/TDD approach those effects on the performance of spectrum and energy efficiency. Co-frequency and CoTime Full Duplex(CCFD) is an effective solution to improve the spectrum and energy efficiency by transmitting and receiving simultaneously in frequency and time domain. While, CCFD communication often face the self-interference issue when communication occurs, simultaneously. Consequently, in this paper a self-interference elimination by physical feedback channel in CCFD for 3-D Beamforming communication scheme is proposed to improve the over-all system performance in terms of energy and spectrum efficiency. The simulation and analytical outcomes demonstrated that the proposed system is superior than the traditional one.展开更多
A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters i...A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.展开更多
At present, with the progress and innovation of technology, the radar communication dual function wave has become a hot research at home and abroad. Excellent integrated waveform can make full use resources of combat ...At present, with the progress and innovation of technology, the radar communication dual function wave has become a hot research at home and abroad. Excellent integrated waveform can make full use resources of combat platform, reduce the size of equipment, and realize the actual functionality of the reality of the battle-field without affecting the radar communication func-tion. The MSK-LFM dual-function wave is a typical representative;it is based on LFM, through the MSK modulation to achieve the integration function. This paper proposes a scheme of combining the spread spectrum technology with the MSK-LFM waveform based on the previous literature. The simulation results show that the waveform envelope is more stable and the energy is more concentrated. With the introduction of spread-spectrum technology, the new waveform ambiguity function graph is much closer to the thumbtack than the traditional MSK-LFM waveform.展开更多
Recently, due to the deployment flexibility of unmanned aerial vehicles(UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assiste...Recently, due to the deployment flexibility of unmanned aerial vehicles(UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assisted mobile relay communication system is limited by the capacity of wireless backhaul link between base station and UAV. In this paper, we consider a caching UAV-assisted decode-and-forward relay communication system in a downlink maritime communication. For the general case with multiple users, the optimal placement of UAV is obtained by solving the average achievable rate maximization problem through the one-dimensional linear search. For a special case with single user, we derive a semi closedform expression of the optimal placement of UAV. Simulation results confirm the accuracy of analytical results and show that the optimal placement of UAV and the average achievable rate significantly depend on the cache capacity at UAV. We also show the difference between the performances of the air-to-ground model and the air-to-sea model.展开更多
Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by...Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.展开更多
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
文摘In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.
文摘With the boom in maritime activities,the need for highly reliable maritime communication is becoming urgent,which is an important component of 5G/6G communication networks.However,the bandwidth reuse characteristic of 5G/6G networks will inevitably lead to severe interference,resulting in degradation in the communication performance of maritime users.In this paper,we propose a safe deep reinforcement learning based interference coordination scheme to jointly optimize the power control and bandwidth allocation in maritime communication systems,and exploit the quality-of-service requirements of users as the risk value references to evaluate the communication policies.In particular,this scheme designs a deep neural network to select the communication policies through the evaluation network and update the parameters using the target network,which improves the communication performance and speeds up the convergence rate.Moreover,the Nash equilibrium of the interference coordination game and the computational complexity of the proposed scheme are analyzed.Simulation and experimental results verify the performance gain of the proposed scheme compared with benchmarks.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
文摘Unmanned Aerial Vehicle(UAV)-aided communication,prized for its network reconfigurability,operational flexibility,and cost-effectiveness,is a key enabler of the low-altitude economy.However,the high possibilities of line-of-sight links and the broadcast nature of air-ground UAV communications make it vulnerable and prone to eavesdropping by malicious nodes.
基金supported by the National Natural Science Foundation of China under Grants No.61671367 and 62471381the Research Foundation of Science and Technology on Communication Networks Laboratory,and the National Key Laboratory of Wireless Communications Foundation under Grant No.IFN202401.
文摘Satellite communication plays an important role in 6G systems.However,satellite communication systems are more susceptible to intentional or unintentional interference signals than other communication systems because of their working mechanism of transparent forwarding.For the purpose of eliminating the influence of interference,this paper develops an angle reciprocal interference suppression scheme based on the reconstruction of interferenceplus-noise covariance matrix(ARIS-RIN).Firstly,we utilize the reciprocity between the known beam central angle and the unknown signal arrival angle to estimate the angle of arrival(AOA)of desired signal due to the multi-beam coverage.Then,according to the priori known spatial spectrum distribution,the interferenceplus-noise covariance matrix(INCM)is reconstructed by integrating within the range except the direction of desired signal.In order to correct the estimation bias of the first two steps,the worst-case performance optimization technology is adopted in the process of solving the beamforming vector.Numerical simulation results show that the developed scheme:1)has a higher output signal-to-interference-plus-noise ratio(SINR)under arbitrary signal-to-noise ratio(SNR);2)still has good performance under small snapshots;3)is robuster and easier to be realized when comparing with minimum variance distortionless response(MVDR)and the traditional diagonal loading algorithms.
基金supported by the National Natural Science Foundation of China under Grant No.62471381the ZTE Industry-University-Institute Cooperation Funds.
文摘In the field of antenna engineering parameter calibration for indoor communication base stations,traditional methods suffer from issues such as low efficiency,poor accuracy,and limited applicability to indoor scenarios.To address these problems,a high-precision and high-efficiency indoor base station parameter calibration method based on laser measurement is proposed.We use a high-precision laser tracker to measure and determine the coordinate system transformation relationship,and further obtain the coordinates and attitude of the base station.In addition,we propose a simple calibration method based on point cloud fitting for specific scenes.Simulation results show that using common commercial laser trackers,we can achieve a coordinate correction accuracy of 1 cm and an angle correction accuracy of 0.25°,which is sufficient to meet the needs of wireless positioning.
基金supported in part by National Natural Science Foundation of China(No.62271080)in part by Fund of State Key Laboratory of IPOC(BUPT)(No.IPOC2022ZT06)in part by BUPT Excellent Ph.D Students Foundation(No.CX2022102).
文摘To achieve a low-complexity nonlinearity compensation(NLC)in high-symbol-rate(HSR)systems,we propose a modified weighted digital backpropagation(M-W-DBP)by jointly shifting the calculated position of nonlinear phase noise and considering the correlation of neighboring symbols in the NLC section of DBP.Based on this model,with the aid of neural network optimization,a learned version of M-W-DBP(M-W-LDBP)is also proposed and explored.Furthermore,enough technical details are revealed for the first time,including the principle of our proposed M-W-DBP and M-W-LDBP,the training process,and the complexity analysis of different DBPclass NLC algorithms.Evaluated numerically with QPSK,16QAM,and PS-64QAM modulation formats,1-step-per-span(1-StPS)M-W-DBP/LDBP achieves up to 1.29/1.49 dB and 0.63/0.74 dB signal-to-noise ratio improvement compared to chromatic dispersion compensation(CDC)in 90-GBaud and 128-GBaud 1000-km single-channel transmission systems,respectively.Moreover,1-StPS M-W-DBP/LDBP provides a more powerful NLC ability than 2-StPS LDBP but only needs about 60%of the complexity.The effectiveness of the proposed M-W-DBP and M-W-LDBP in the presence of laser phase noise is also verified and the necessity of using the learned version of M-WDBP is also discussed.This work is a comprehensive study of M-W-DBP/LDBP and other DBP-class NLC algorithms in HSR systems.
基金supported in part by the National Natural Science Foundation of China under Grants 62001225,62071236,62071234 and U22A2002in part by the Major Science and Technology plan of Hainan Province under Grant ZDKJ2021022+1 种基金in part by the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008in part by the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-2.
文摘The Internet of Unmanned Aerial Vehicles(I-UAVs)is expected to execute latency-sensitive tasks,but limited by co-channel interference and malicious jamming.In the face of unknown prior environmental knowledge,defending against jamming and interference through spectrum allocation becomes challenging,especially when each UAV pair makes decisions independently.In this paper,we propose a cooperative multi-agent reinforcement learning(MARL)-based anti-jamming framework for I-UAVs,enabling UAV pairs to learn their own policies cooperatively.Specifically,we first model the problem as a modelfree multi-agent Markov decision process(MAMDP)to maximize the long-term expected system throughput.Then,for improving the exploration of the optimal policy,we resort to optimizing a MARL objective function with a mutual-information(MI)regularizer between states and actions,which can dynamically assign the probability for actions frequently used by the optimal policy.Next,through sharing their current channel selections and local learning experience(their soft Q-values),the UAV pairs can learn their own policies cooperatively relying on only preceding observed information and predicting others’actions.Our simulation results show that for both sweep jamming and Markov jamming patterns,the proposed scheme outperforms the benchmarkers in terms of throughput,convergence and stability for different numbers of jammers,channels and UAV pairs.
基金supported by the Hainan Province Science and Technology Special Fund,China(No.ZDYF2024GXJS292).
文摘The increasing importance of terminal privacy in the Unmanned Aerial Vehicle(UAV)network has led to a growing recognition of the crucial role of authentication technology in UAV network security.However,traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel.These vulnerabilities include brute-force attacks,single point of failure,and information leakage.Blockchain,as a decentralized distributed ledger with blockchain storage,tamper-proof,secure,and trustworthy features,can solve problems such as single-point-of-failure and trust issues,while the hidden communication in the physical layer can effectively resist information leakage and violent attacks.In this paper,we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication,where the identity information is transmitted as covert tags carried by normal modulated signals.In addition,a weight-based Practical Byzantine Fault-Tolerant(wPBFT)consensus protocol is devised,where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios.Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness,particularly under conditions of low Signal-to-Noise Ratio(SNR)and short tag length.
基金supported by the National Natural Science Foundation of China(Nos.U23A20271 and 62325103).
文摘Unmanned aerial vehicles(UAVs),characterized by their low cost and operational flexibility,have been increasingly deployed across civilian,military,and commercial applications.To improve the coverage and connectivity,UAVs can be utilized to realize the comprehensive spatial coverage for the sixth-generation mobile networks.However,the private data in UAV networks is easy to be exposed due to the light-of-sight links and openness of wireless transmission.Covert communication as an emerging technique has shown its superiority in hiding the transmission behavior,which can further enhance the security of UAV networks compared with the traditional physical-layer security.Therefore,in this article,we present a survey on the recent advanced research about covert UAV communications.First,the roles of UAVs for covert communications are described.Then,the covert UAV communications with different uncertainties are introduced.Moreover,the wireless techniques for covert UAV communications are explored.In addition,we point out the applications in covert UAV communications.Finally,the open research issues concerning practical scenarios and promising applications are highlighted.
基金co-supported by the National Natural Science Foundation of China(Nos.62301431 and U22B2013)the Guangdong Basic and Applied Basic Research Foundation,China(No.2024A1515030215)+3 种基金the Key Research and Development Program of Shaanxi Province,China(No.2023-GHZD-05)the Innovation Capability Support Program of Shaanxi Province,China(No.2021TD-08)the National Key Laboratory of Wireless Communications Foundation,China(No.IFN20230111)the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-24-04).
文摘Reconfigurable Intelligent Surfaces(RISs)enable programmable wireless environments and thus have great potential for enhancing physical layer security.However,the security gain of conventional passive RISs is often limited by the“multiplicative fading”effect through reflection links,which becomes severe in the case of double reflections and significantly degrades the security performance.In this paper,we consider a wireless system that consists of a fixed passive RIS and an Unmanned Aerial Vehicle(UAV)-mounted active RIS,where the UAV-enabled aerial amplification and reflection are exploited to compensate for the multiplicative fading effect.We formulate the problem to maximize the secrecy rate by jointly considering the optimal deployment of the UAV-based active RIS and the reflection coefficients at both the passive and active RISs.To enable efficient algorithm design,we decompose the problem into two layers:the outer layer optimizes the UAV deployment through deep reinforcement learning,while the inner layer solves the beamforming and reflection design using a block coordinate descent framework.Simulation results demonstrate the convergence of the proposed learning procedure,and indicate that the active RIS with learned deployment can effectively enhance the reflection and significantly improve the secrecy rate.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金supported by the National Natural Science Foundation of China (61073183)the Natural Science Foundation for the Youth of Heilongjiang Province (QC2012C070)
文摘In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.
基金supported by National Natural Science Foundation of China (Nos.61172107,61172110)National High Technical Research and Development Program (863 Program) of China (No.2015AA016306)+1 种基金Major Projects in Liaoning Province Science and Technology Innovation (No.201302001)Fundamental Research Funds for the Central Universities of China (No.DUT13LAB06)
文摘The 3-D beamforming scheme has elite as evolving interest because of its efficiency to empower assorted techniques such as vertical and horizontal domains and emanation beamforming according to subscriber's provisions. Usually, 3-D beamforming communication is set up on FDD/TDD approach those effects on the performance of spectrum and energy efficiency. Co-frequency and CoTime Full Duplex(CCFD) is an effective solution to improve the spectrum and energy efficiency by transmitting and receiving simultaneously in frequency and time domain. While, CCFD communication often face the self-interference issue when communication occurs, simultaneously. Consequently, in this paper a self-interference elimination by physical feedback channel in CCFD for 3-D Beamforming communication scheme is proposed to improve the over-all system performance in terms of energy and spectrum efficiency. The simulation and analytical outcomes demonstrated that the proposed system is superior than the traditional one.
基金supported by the National Natural Science Foundation of China(61401196)the Jiangsu Provincial Natural Science Foundation of China(BK20140954)+1 种基金the Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(KX152600015/ITD-U15006)the Beijing Shengfeifan Electronic System Technology Development Co.,Ltd(KY10800150036)
文摘A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.
文摘At present, with the progress and innovation of technology, the radar communication dual function wave has become a hot research at home and abroad. Excellent integrated waveform can make full use resources of combat platform, reduce the size of equipment, and realize the actual functionality of the reality of the battle-field without affecting the radar communication func-tion. The MSK-LFM dual-function wave is a typical representative;it is based on LFM, through the MSK modulation to achieve the integration function. This paper proposes a scheme of combining the spread spectrum technology with the MSK-LFM waveform based on the previous literature. The simulation results show that the waveform envelope is more stable and the energy is more concentrated. With the introduction of spread-spectrum technology, the new waveform ambiguity function graph is much closer to the thumbtack than the traditional MSK-LFM waveform.
基金supported in part by the Natural Science Foundation of China under Grant U1805262,61671251,61871446,61701118,61871131,and 61404130218the Natural Science Foundation of Fujian Province under Grant 2018J05101。
文摘Recently, due to the deployment flexibility of unmanned aerial vehicles(UAVs), UAV-assisted mobile relay communication system has been widely used in the maritime communication. However, the performance of UAV-assisted mobile relay communication system is limited by the capacity of wireless backhaul link between base station and UAV. In this paper, we consider a caching UAV-assisted decode-and-forward relay communication system in a downlink maritime communication. For the general case with multiple users, the optimal placement of UAV is obtained by solving the average achievable rate maximization problem through the one-dimensional linear search. For a special case with single user, we derive a semi closedform expression of the optimal placement of UAV. Simulation results confirm the accuracy of analytical results and show that the optimal placement of UAV and the average achievable rate significantly depend on the cache capacity at UAV. We also show the difference between the performances of the air-to-ground model and the air-to-sea model.
文摘Unmanned Aerial Vehicle(UAV)has emerged as a promising novel application for the Sixth-Generation(6G)wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation.However,the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication.This paper investigates the trajectory planning problem in an UAV communication system,where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jammers with imperfect power and location information.To ensure the reliability of the GCU-to-UAV link,we formulate the problem as a non-convex semi-infinite optimization,aiming to maximize the average worst-case Signal-toInterference-plus-Noise Ratio(SINR)over a given flight duration by designing the robust trajectory of the UAV under stringent energy availability constraints.To handle this problem efficiently,we develop an iterative algorithm for the solution with the aid of S-procedure and Successive Convex Approximation(SCA)method.Numerous results demonstrate the efficacy of our proposed algorithm and offer some useful design insights to practical system.
