For the problem that the existing deep learning modulation recognition algorithms are not robust enough and have insufficient generalization ability in complex signal environments，a multi-channel network based on phase parameter estimation and spatial reconstruction(PET-SAMCL) is proposed. First，the input in-phase quadature(IQ) signal is converted by phase parameter estimation and divided into three modules to extract the amplitude-phase feature，IQ combination and branching features of IQ respectively. A spatial reorganization unit(SRU) is added to the feature extraction module to reduce the influence of redundant features. The spatial features are refined and fused by global average pooling and soft attention operations，and the temporal and spatial features are extracted by gated recurrent units(GRU) and bidirectional gated recurrent units(BiGRU) . Ablation study determines the optimal model structure. The model performs well on the RML2016.10a dataset，achieving a maximum recognition accuracy of 93.9% at 14 dB，and the average recognition rate is increased by 7.7% compared with that of other models.

For the problems of limited expression of characteristic information and low classification accuracy in radiation source classification tasks,an individual radiation source recognition method based on multi-resolution feature fusion is proposed. In this method, the individual characteristics of the radiation source are expressed by using three time-frequency spectra with different resolutions obtained through the Short-Time Fourier Transform. Multi-channel convolutional neural networks are constructed using ResNext50 to extract features with different time-frequency resolutions. A multi-channel feature weighted fusion mechanism is introduced into the network,and the features of different channels are fused by feature weighted fusion, combining the feature information from different resolutions. Experiments show that this method improves the ability to express the subtle fingerprint information of the radiation source signal,and compared with that of the feature layer fusion method and the single feature expression method, the recognition accuracy is improved by 2.15% and 6.8% ,respectively.

In response to challenges in sea clutter modeling within the classical algorithms,including the lack of fitting accuracy due to the inability to satisfy multiple statistical characteristics simultaneously and the limitations in controllably generating accurate class-based results,combining the generative power of U-Net with the potential of complex-valued neural networks to deal with complex nonlinear problems in the electromagnetic domain, a novel approach is proposed. This approach integrates complex-valued network layers and a classifier-free guidance module, establishing an interpretable mapping mechanism for input conditions,resulting in complex-valued guided diffusion model(CVG-DM). This model is centered on the direct utilization of the complex-valued baseband signals from the In-phase and Quadrature(IQ) path of sea clutter, as well as the exploration of the relationship between sea clutter and strong targets in the background. This enables controlled generation of the model under varying conditions of target presence or absence, and assessment based on amplitude distribution, temporal and spatial correlation, nonlinear characteristics,and Doppler spectrum. Simulation experiment validates CVG-DM's capability in realizing sea clutter data augmentation under varying conditions. The simulated clutter can simultaneously take into account above five statistical properties, surpassing the completeness of real number network-based evaluation metrics and further enhancing fidelity.

For the problem that conventional signal analysis methods are prone to cause the“increasing batch”and“missing batch”in multi-functional radar signal sorting in complex electromagnetic environments, a multi-functional radar signal sorting method based on improved complex network community detection is proposed. This method first transforms the signal sequence into the complex network using limited penetration visibility graph. Then, it introduces spatial clustering with density to eliminate spurious pulses. Subsequently, the label propagation algorithm is improved according to the between centrality of nodes,enhancing the stability of community division. Finally,the sub-communities are merged to complete the signal sorting task by density peak clustering. Simulation results show that the proposed method achieves a sorting accuracy of 98.13% for multi-functional radar signals. Moreover,even when the proportion of spurious pulses increases to 35% , the number of sorted batches remains unchanged, effectively alleviating the“increasing batch”and“missing batch”problems.

In view of the problems of false detection and missed detection when an unmanned aerial vehicle (UAV) detects targets at different scales,a YOLOv8-FDT UAV algorithm model with a multi-scale fusion mechanism is proposed. First, a dynamic upsampling module is added to the Neck layer of the baseline model to reduce the number of model parameters and improve the real-time performance of the model for target recognition. In addition, in order to enable the entire algorithm model to capture different scale semantic information of the target in the feature fusion stage,adaptive downsampling and depth convolution are integrated to design the feature diffusion pyramid network(FDPN). Finally,experiments on the UAV aerial photography dataset VisDrone2019 show that the mean average precision(mAP) of all categories of the improved model is increased by 6.24% compared with that of the baseline model.

Vehicular edge computing(VEC) converges the computing resources of cloud servers to the edge of the network closer to the vehicle side, allowing vehicles to offload vehicular computing tasks to the network edge servers,thus providing vehicles with low latency,high bandwidth and high reliability services. However,the highly dynamic network topology,strict low-delay constraints,and massive data of vehicular tasks of VEC pose significant challenges for implementing efficient offloading. The digital twin(DT)-driven VEC model can enable real-time monitoring of the state of the VEC network,thus assisting in making sound offloading decisions in the physical world. Firstly, the research progress of edge computing, available vehicles and DT-driven VEC task offloading methods are introduced. Then,the scenario architecture of DT-driven task offloading for VEC is elaborated. Finally,the future research challenges and solutions of DT-driven VEC task offloading methods are discussed,in hope of providing certain guidance for solving the problem of DT-driven VEC task offloading.

To address the issue of synthetic aperture radar(SAR) images being susceptible to environmental noise,which leads to a reduction in the signal-to-noise ratio(SNR) at target locations,a method integrating singular value decomposition (SVD) and minimum entropy deconvolution (MED) to enhance the Back Projection(BP ) algorithm is proposed. Initially,the acquired echo signals undergo SVD, and a singular value matrix is obtained. By retaining only the first five singular values and reconstructing the echo signal matrix,an initial noise reduction is achieved. Subsequently,the signals are processed using MED filtering, where filter coefficients are iteratively updated to minimize the entropy of the signal,thereby reducing the kurtosis of the output signal and suppressing noise. A Zero-Phase Filter(ZPF) is then applied to restore any phase delays. Finally, the SAR image is generated using the BP algorithm. Experimental results demonstrate that this method significantly enhances the SNR at target locations,with improvements of 7.9 dB and 9.1 dB for large and small corner reflectors,respectively.

In broadband reconnaissance scenarios，achieving high signal detection accuracy often entails significant computational costs. To address this，a multi-scale convolution attention sparse detection(MSCAS) method is proposed，which incorporates prior knowledge of signal spectrograms by capturing long-range temporal dependencies and suppressing irrelevant frequency-domain interference. MSCA-S introduces a multiscale horizontal convolution attention(MSHCA) mechanism that jointly extracts multi-dimensional signal features，enhancing detection accuracy while reducing computational complexity through horizontal convolution. Building on MSHCA，a hierarchically stacked broadband signal detection framework is developed，and sparse feature parameters are used to further optimize computational efficiency. MSCA-S is evaluated on a real-world and simulated broadband signal dataset(2.5 MHz spectrum) collected in Qingdao，achieving an average detection accuracy of 95.6% across varying signal-to-noise ratios. Compared with the frequency-sensitive signal detector，the Swin-Transformer-based protocol recognition method，and the Res-101 detection method，MSCA-S improves accuracy by 0.05%，2.94%，and 6.14%，respectively，while reducing computational costs by 1.53×10¹⁰，1.79×10¹⁰，and 4.59×10¹⁰，respectively.

In order to reduce complexity of construction of indoor positioning fingerprint database and improve the positioning accuracy, an indoor fingerprint positioning algorithm based on matrix completion under the 5G ultra-dense network is proposed. In the offline database construction stage,the algorithm first uses the K-nearest Neighbor(KNN) interpolation method to complete the matrix of part of the fingerprint database to construct a complete database. Secondly,the sparse auto-encoder is used to extract the sparse features of the fingerprint database, and the high-dimensional received signal strength indication (RSSI) signal is reduced. In the online fingerprint matching stage,the weighted KNN algorithm is used to estimate the coordinates of the point to be located. After experimental simulation,the average relative error of the algorithm to reconstruct the fingerprint database is 0.31% . Compared with that of the traditional KNN fingerprint matching algorithm,the average error is reduced by 24.41% .

In order to achieve low-latency and low-energy offshore communication, the dynamic service cache update mechanism is introduced into the complex neural network, and the mobile edge dynamic service caching policy (MEDSCP ) based on double deep Q network (DDQN ) is proposed by cleverly designing the complex neural network structure based on offshore communication scenarios. The policy firstly obtains the optimal offloading decision set through the user terminal task offloading decision game, and then utilizes mobile edge computing(MEC) and dynamic service caching update to reduce the delay and energy cost of task execution in the offshore communication environment, aiming to improve the efficiency of task processing in offshore communication and to expand the development potential of this industry. Simulation experimental results show that the proposed MEDSCP strategy can achieve fast convergence of the algorithm while guaranteeing the training effect,and also effectively reduce the delay-energy weighted sum of offshore communications compared with existing work.