The bistatic Integrated Sensing and Communication (ISAC) is poised to become a key application for next generation communication networks (e.g., B5G/6G), providing simultaneous sensing and communication services with minimal changes to existing network infrastructure and hardware. However, a significant challenge in bistatic cooperative sensing is clock asynchronism, arising from the use of different clocks at far separated transmitters and receivers. This asynchrony leads to Timing Offsets (TOs) and Carrier Frequency Offsets (CFOs), potentially causing sensing ambiguity. Traditional synchronization methods typically rely on static reference links or GNSS-based timing sources, both of which are often unreliable or unavailable in UAVbased bistatic ISAC scenarios. To overcome these limitations, we propose a Time-Varying Offset Estimation (TVOE) framework tailored for clock-asynchronous bistatic ISAC systems, which leverages the geometrically predictable characteristics of the Line-of-Sight (LoS) path to enable robust, infrastructure-free synchronization. The framework treats the LoS delay and the Doppler shift as dynamic observations and models their evolution as a hidden stochastic process. A state-space formulation is developed to jointly estimate TO and CFO via an Extended Kalman Filter (EKF), enabling real-time tracking of clock offsets across successive frames. Furthermore, the estimated offsets are subsequently applied to correct the timing misalignment of all Non-Line-of-Sight (NLoS) components, thereby enhancing the high-resolution target sensing performance. Extensive simulation results demonstrate that the proposed TVOE method improves the estimation accuracy by 60

As smart cities rapidly evolve, the Vehicle-toEverything (V2X) network faces significant challenges in data security and communication efficiency. This paper introduces an innovative algorithm based on a reputation mechanism, named the Dynamic Vehicle Reputation Consensus (DVRC), which focuses on improving the data security and communication efficiency of vehicular networks using blockchain technology. The DVRC algorithm comprehensively assesses vehicle behaviors and the consensus contribution within the blockchain network, utilizing a reputation scoring system to evaluate the reliability within the network. Furthermore, this study delves into multimodal communication strategies in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) scenarios. Notably, when vehicles leave the range of basic infrastructure, those with high reputation scores (as determined by the DVRC's reputation values) relay the communication service. Additionally, the introduction of a reputation incentive mechanism and dynamic consensus threshold adjustments aim to improve consensus efficiency and encourage honest behavior. Experimental results demonstrate that the DVRC algorithm significantly improves communication efficiency in blockchain-based vehicular networks, such as increased throughput, reduced latency, and improved network scalability. These advances have substantial theoretical and practical significance for the development of vehicular networks in smart cities

With the rapid development of Internet of Things (IoT) technology, interconnectivity between devices has become increasingly widespread. However, traditional IoT security measures struggle to cope with increasingly complex security threats and cannot fully exploit the advantages of interconnectivity due to the limited computational resources of the devices. To address this, we propose a blockchain-based IoT security framework comprising wallet component, smart contract component, multilayer security component, and common component. This framework, designed for resource-constrained environments and embedded into cellular communication modules, enables multiend offloading of computational tasks and secure transmission for IoT devices. Experimental results show that the data processing capability of the decentralized network architecture based on this framework is improved by 115.06% compared to traditional methods, enhances the security and autonomy of IoT devices, and significantly strengthens the degree of IoT decentralization. This provides a valuable reference for designing next-generation IoT security architectures.

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported "black flight" UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC's potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.

Recent advances in the design of convolutional neural networks have shown that performance can be enhanced by improving the ability to represent multi-scale features. However, most existing methods either focus on designing more sophisticated attention modules, which leads to higher computational costs, or fail to effectively establish long-range channel dependencies, or neglect the extraction and utilization of structural information. This work introduces a novel module, the Multi-Branch Concatenation (MBC), designed to process input tensors and extract multi-scale feature maps. The MBC module introduces new degrees of freedom (DoF) in the design of attention networks by allowing for flexible adjustments to the types of transformation operators and the number of branches. This study considers two key transformation operators: multiplexing and splitting, both of which facilitate a more granular representation of multi-scale features and enhance the receptive field range. By integrating the MBC with an attention module, a Multi-Branch Attention (MBA) module is developed to capture channel-wise interactions within feature maps, thereby establishing long-range channel dependencies. Replacing the 3x3 convolutions in the bottleneck blocks of ResNet with the proposed MBA yields a new block, the Efficient Multi-Branch Attention (EMBA), which can be seamlessly integrated into state-of-the-art backbone CNN models. Furthermore, a new backbone network, named EMBANet, is constructed by stacking EMBA blocks. The proposed EMBANet has been thoroughly evaluated across various computer vision tasks, including classification, detection, and segmentation, consistently demonstrating superior performance compared to popular backbones.

Bistatic Integrated Sensing and Communication (ISAC) is poised to become a cornerstone technology in next-generation communication networks, such as Beyond 5G (B5G) and 6G, by enabling the concurrent execution of sensing and communication functions without requiring significant modifications to existing infrastructure. Despite its promising potential, a major challenge in bistatic cooperative sensing lies in the degradation of sensing accuracy, primarily caused by the inherently weak received signals resulting from high reflection losses in complex environments. Traditional methods have predominantly relied on adaptive filtering techniques to enhance the Signal-to-Noise Ratio (SNR) by dynamically adjusting the filter coefficients. However, these methods often struggle to adapt effectively to the increasingly complex and diverse network topologies. To address these challenges, we propose a novel Image Super-Resolution-based Signal Enhancement (ISR-SE) framework that significantly improves the recognition and recovery capabilities of ISAC signals. Specifically, we first perform a time-frequency analysis by applying the Short-Time Fourier Transform (STFT) to the received signals, generating spectrograms that capture the frequency, magnitude, and phase components. These components are then mapped into RGB images, where each channel represents one of the extracted features, enabling a more intuitive and informative visualization of the signal structure. To enhance these RGB images, we design an improved denoising network that combines the strengths of the UNet architecture and diffusion models. This hybrid architecture leverages UNet's multi-scale feature extraction and the generative capacity of diffusion models to perform effective image denoising, thereby improving the quality and clarity of signal representations under low-SNR conditions.

With the deployment of large-scale antenna arrays, the already limited time-frequency resources are becoming increasingly scarce. In this study, we propose a novel Laplacian Pyramid Channel Completion Network (LPCCNet) designed for channel completion, thereby reducing the demand for time-frequency resources in massive MIMO systems. Compared with existing network models, the proposed LPCCNet, by employing a progressive upsampling architecture, effectively mitigates aliasing effects, suppresses error propagation, and achieves a substantial reduction in computational complexity. The simulation results show that LPCCNet achieves a superior channel completion quality compared to existing methods, particularly in rapidly time-varying scenarios.

Environmental factors and electronic interference often disrupt communication between UAV swarms and ground control centers, requiring UAVs to complete missions autonomously in offline conditions. However, current coordination schemes for UAV swarms heavily depend on ground control, lacking robust mechanisms for offline task allocation and coordination, which compromises efficiency and security in disconnected settings. This limitation is especially critical for complex missions, such as rescue or attack operations, underscoring the need for a solution that ensures both mission continuity and communication security. To address these challenges, this paper proposes an offline task coordination algorithm based on blockchain smart contracts. This algorithm integrates task allocation, resource scheduling, and coordination strategies directly into smart contracts, allowing UAV swarms to autonomously make decisions and coordinate tasks while offline. Experimental simulations confirm that the proposed algorithm effectively coordinates tasks and maintains communication security in offline states, significantly enhancing the swarm’s autonomous performance in complex, dynamic scenarios.

With the deployment of large-scale antenna arrays, wireless channels have become sufficiently sparse that they can be analogized to an image. Consequently, the interdisciplinary integration of deep learning and channel estimation has emerged as a new research direction. In this paper, a novel channel image generation method is developed and a deep learning compressed sensing-based channel estimation network (CSCENet) is proposed for massive MIMO systems using a data-driven approach. Simulation results show that the proposed CSCENet can achieve a good performance at a large dynamically changing SNR range. Especially at low SNRs, considerable gains can be observed as compared to the benchmark channel estimation algorithm of linear minimum mean squared error (LMMSE).

Low-dose computed tomography (LDCT) contains the mixed noise of Poisson and Gaussian, which makes the image reconstruction a challenging task.In order to describe the statistical characteristics of the mixed noise, we adopt the sinogram preprocessing as a standard maximum a posteriori (MAP).Based on the fact that the sinogram of LDCT has nonlocal self-similarity property, it exhibits low-rank characteristics.The conventional way of solving the low-rank problem is implemented in matrix forms, and ignores the correlations among similar patch groups.To avoid this issue, we make use of a nonlocal Kronecker-Basis-Representation (KBR) method to depict the low-rank problem.A new denoising model, which consists of the sinogram preprocessing for data fidelity and the nonlocal KBR term, is developed in this work.The proposed denoising model can better illustrate the generative mechanism of the mixed noise and the prior knowledge of the LDCT.Numerical results show that the proposed denoising model outperforms the state-of-the-art algorithms in terms of peak-signal-to-noise ratio (PSNR), feature similarity (FSIM), and normalized mean square error (NMSE).

Millimeter wave (MmWave) has become the most promising candidate to enable multi-Gbps transmission and high-precision detection due to the large available bandwidth. Because of the serious propagation attenuation, it is necessary for a vehicle-to-infrastructure (V2I) system communicating in the mmWave band to overcome severe path loss by utilizing beamforming technology. However, swift and accurate beam alignment at the transceivers is challenging when considering user mobility and fast-varying wireless environment. In this paper, we propose an Adaptive Discounted Thompson Sampling (ADTS) based beam alignment algorithm without any prior information such as coarse user location information, which ignores historical observations made beyond the past time periods and avoids misleading for the current state. Besides, we conduct performance analysis to determine the achievable performance bound of the proposed algorithm. Using simulation results, we show that our proposed algorithm achieves good performance in terms of the average effective achievable rate and the beam alignment accuracy without prior knowledge.

随着大规模天线阵在基站端的部署,信道矩阵变得越来越稀疏,因此传统的信道矩阵具备了图像的特性,可以将稀疏的信道矩阵视为二维自然图像,借助深度学习的网络模型进行感知和估计.提出了一种新型信道图像的生成方式,解决了传统信道图像的获取依赖于天线几何尺寸的问题.更进一步地,借鉴深度学习在图像降噪方面的应用,提出了一种基于深度学习去噪的信道感知网络模型,将带噪声的信道矩阵视为信道图像,作为输入张量,通过深度去噪网络对噪声进行学习和消除,输出干净的信道图像作为信道感知的结果.仿真结果表明,与LMMSE基准算法相比,所提基于深度去噪的信道感知网络模型在超低信噪比下具有更好的性能.在高信噪比下,可以达到近似LMMSE的性能,且具备更低的实现复杂度.

In this paper, we develop simultaneous detection techniques of signals from multiple users for uplink multi-user MIMO (UL MU-MIMO) systems. Conventional detectors do not take the detection delay into account. Two parallelizing access methods are proposed for UL MU-MIMO systems. The multiple uplink users can be detected in parallel after the parallelizing process. Moreover, the multiple uplink users can be scheduled on the same radio frequency resource as if all the other users did not exist. Therefore, the proposed detection methods can scale up with the system dimensions by keeping the bit error rate (BER) and the detection delay at an acceptable level. Simulation results show that the proposed detection methods via parallel access achieve considerable BER gains with much less detection delay as compared to their conventional counterparts.

Due to the much higher crosstalk impairing in G.fast high-frequency channels compared with digital subscriber line low-frequency channels, the nonlinear Tomlinson-Harashima precoding (THP) has a good potential to enhance the G.fast system performance. However, ordering plays an important role for the data rate performance in THP. The users precoded earlier experience enhanced data rate performance, while the users precoded later are punished with lower data rates. This would be undesirable from the service providers' point of view. In this letter, a systemic rate balancing method named projected sorting is developed by sorting the G.fast lines with respect to the projected channel vectors. Simulation results show that the proposed projected sorting can achieve performance among users in a more controlled way.

In this paper, we propose novel non-linear precoders for the downlink of a multi-user MIMO system in the existence of multiple eavesdroppers. The proposed non-linear precoders are designed to improve the physical-layer secrecy rate. Specifically, we combine the non-linear successive optimization Tomlinson-Harashima precoding (SO-THP) with the generalized matrix inversion (GMI) technique to maximize the physical-layer secrecy rate. For the purpose of comparison, we examine different traditional precoders with the proposed algorithm in terms of secrecy rate as well as bit error rate (BER) performance. We also investigate simplified generalized matrix inversion (S-GMI) and lattice-reduction (LR) techniques in order to efficiently compute the parameters of the precoders. We further conduct computational complexity and secrecy-rate analysis of the proposed and existing algorithms. In addition, in the scenario without knowledge of the channel state information (CSI) to the eavesdroppers, a strategy of injecting artificial noise (AN) prior to the transmission is employed to enhance the physical-layer secrecy rate. Simulation results show that the proposed non-linear precoders outperform existing precoders in terms of BER and secrecy-rate performance.

In this paper, we propose novel non-linear precoders for the downlink of a multi-user MIMO system with the existence of multiple eavesdroppers. The proposed non-linear precoders are designed to improve the physical-layer secrecy rate. Specifically, we combine the non-linear successive optimization Tomlinson-Harashima precoding (SO-THP) with generalized matrix inversion (GMI) technique to maximize the physical-layer secrecy rate. For the purpose of comparison, we examine different traditional precoders with the proposed algorithm in terms of secrecy rate as well as BER performance. We also investigate simplified generalized matrix inversion (S-GMI) and lattice-reduction (LR) techniques in order to efficiently compute the parameters of the precoders. We further conduct computational complexity and secrecy rate analysis of the proposed and existing algorithms. In addition, in the scenario without knowledge of channel state information (CSI) to the eavesdroppers, a strategy of injecting artificial noise (AN) prior to the transmission is employed to enhance the physical-layer secrecy rate. Simulation results show that the proposed non-linear precoders outperform existing precoders in terms of BER and secrecy rate performance.

In this paper, we propose novel non-linear precoders for the downlink of a multi-user MIMO system with the existence of multiple eavesdroppers. The proposed non-linear precoders are designed to improve the physical-layer secrecy rate. Specifically, we combine the non-linear successive optimization TomlinsonHarashima precoding (SO-THP) with generalized matrix inversion (GMI) technique to maximize the physical-layer secrecy rate. For the purpose of comparison, we examine different traditional precoders with the proposed algorithm in terms of secrecy rate as well as BER performance. We also investigate simplified generalized matrix inversion (S-GMI) and latticereduction (LR) techniques in order to efficiently compute the parameters of the precoders. We further conduct computational complexity and secrecy rate analysis of the proposed and existing algorithms. In addition, in the scenario without knowledge of channel state information (CSI) to the eavesdroppers, a strategy of injecting artificial noise (AN) prior to the transmission is employed to enhance the physical-layer secrecy rate. Simulation results show that the proposed non-linear precoders outperform existing precoders in terms of BER and secrecy rate performance.

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side to implement the concept of dirty paper coding (DPC). The perform of THP, however, is restricted by the dimensionality constraint that the number of transmit antennas has to be greater or equal to the total number of receive antennas. In this paper, we propose an iterative coordinate THP algorithm for the scenarios in which the total number of receive antennas is larger than the number of transmit antennas. The proposed algorithm is implemented on two types of THP structures, the decentralized THP (dTHP) with diagonal weighted filters at the receivers of the users, and the centralized THP (cTHP) with diagonal weighted filter at the transmitter. Simulation results show that a much better bit error rate (BER) and sum-rate performances can be achieved by the proposed iterative coordinate THP compared to the previous linear art.

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side to implement the concept of dirty paper coding (DPC). The application of THP is restricted by the dimensionality constraint that the number of transmit antennas has to be greater or equal to the total number of receive antennas. In this paper, we propose an iterative coordinate THP algorithm for overloaded scenarios in which the total number of receive antennas is larger than the number of transmit antennas. The proposed algorithm is implemented on two types of THP structures, the decentralized THP (dTHP) with diagonal weighted filters at the receivers of the users, and the centralized THP (cTHP) with diagonal weighted filter at the transmitter. Simulation results show that a significantly better bit error rate (BER) and sum-rate performances can be achieved by the proposed iterative coordinate THP algorithm as compared to previously reported techniques.

The application of precoding algorithms in multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems is restricted by the dimensionality constraint that the number of transmit antennas has to be greater than or equal to the total number of receive antennas. In this paper, a lattice reduction (LR)-aided flexible coordinated beamforming (LR-FlexCoBF) algorithm is proposed to overcome the dimensionality constraint in overloaded MU-Massive-MIMO systems. A random user selection scheme is integrated with the proposed LR-FlexCoBF to extend its application to MU-Massive-MIMO systems with arbitary overloading levels. Simulation results show that significant improvements in terms of bit error rate (BER) and sum-rate performances can be achieved by the proposed LR-FlexCoBF precoding algorithm.