Due to the uniqueness and stability of radio frequency fingerprints (RFF), radio frequency fingerprint identification (RFFI) is an important physical layer authentication method in the security of Internet of Things (IoT). However, existing deep-learning-based RFFI methods require a large number of labeled samples to achieve ideal performance. Besides, when the signal length changes, the network structure needs to be redesigned and the entire training process needs to be reconducted. To address this issue, we propose a few shot RFFI method on the basis of self-distillation with no labels (SDINO). Within it, a network termed DPformer is designed, which can adapt signals of varying lengths and is more lightweight. When the signal length changes, there is no need to retrain the network, and it is more lightweight. Simulation results show that, compared with existing methods, the proposed method achieves better recognition performance and more lightweight on LoRa dataset with 30 classes.

Underwater wireless optical communication (UWOC) is a promising technology for the massive data transmission of the Internet of Underwater Things (IoUT) due to its high speed, low latency, and wide range of applications. However, UWOC systems face significant challenges, such as oceanic turbulence and pointing errors. Given the increasing number of underwater optical devices, interference is another challenge for the UWOC. Therefore, this paper proposes a multihop UWOC system based on rate-splitting multiple access (RSMA) with relay selection over the Weibull-generalized Gamma (WGG)-distributed oceanic thermocline turbulence channel with pointing error. The RSMA scheme is used to realize communication between a source and multiple users. Multihop relays are deployed to extend the communication distance, and relay selection is applied to improve the channel quality of underwater wireless optical links. The statistical characterization of the system is analyzed, including the probability density function (PDF) and the cumulative distribution function (CDF) of the instantaneous signal-to-noise ratio (SNR) under relay selection. Based on the statistical results, an exact closed-form expression for the outage probability is given. Moreover, the asymptotic expression of the outage probability under high SNR is provided, and the diversity order is obtained based on the asymptotic analysis of the outage probability. Simulation results are given to verify our derived expressions. The outage performance of the proposed system under various system configurations, including air bubble levels, thermohaline gradients, pointing error levels, target rates, power allocation, the number of parallel relays, and the number of hops, is analyzed. Additionally, the RSMA-based system is compared with the non-orthogonal multiple access (NOMA)-based system. It has been demonstrated that the RSMA-based system outperforms the NOMA-based system.

Deep learning-based Synthetic Aperture Radar (SAR) ship image processing is essential in both civilian and military applications. However, training deep learning models requires a large dataset of high-resolution SAR ship images. Since SAR sensors are not readily available, obtaining an adequate number of such images remains a significant challenge. The SAR image generation technique is a useful way to create SAR ship images. However, many current methods depend only on location maps as input and do not incorporate guidance for the content of the SAR images, resulting in the production of low-quality images. To tackle these issues, we propose a novel content guided high resolution SAR ship image generation method. This approach incorporates SAR image-specific information to enhance the details of the generated images. We achieve this by separately fusing the input source image with both the source and target location maps, using them as guidance to steer the generation process. Additionally, to effectively transfer this guidance to the target SAR image, we introduce a Feature Transferring Module, which performs affine transformations based on scaling and shifting values learned by a Parameter Learning Module. Moreover, we design a Mask Transformer Module to learn the correlation weight and establish a fine-grained mapping between the source and target SAR images. To train and evaluate the proposed model, we have re-labeled the land and sea regions in the HRSID dataset for comparative analysis. Extensive experiments demonstrate that our model performs exceptionally well in the high-resolution SAR image generation task.

In the field of synthetic aperture radar (SAR) image analysis, the main challenges include the difficulty of eliminating the effects of ambient noise, the variability of objects, and the distinction between targets and nontargets. To address these issues, we propose a novel detection model, VFMDet, based on brain-inspired visual filtering mechanism. The model comprises two primary components: a brain-inspired filtering module and a SAR ship detection module. The former contains a bottom-up filtering module responsible for low-level feature extraction, an up-bottom filtering module responsible for high-level feature extraction, and a brain-inspired fusion module responsible for fusing the original image with the filtered feature map. In the SAR ship detection process, to accurately regress the orientation of the ship, we introduce a five-point coding scheme in polar coordinate system. Meanwhile, we introduce a Gaussian heatmap strategy (GHS) that utilizes limited covariance and a Gaussian heatmap loss to solve the problem caused by large-scale variation and dense arrangement of ship target. Finally, we design a multitask loss to help the model complete the end-to-end training. We conducted experiments on the rotating SAR ship detection dataset (RSSDD) and the rotating ship detection dataset (RSDD), and the mean average precision (mAP) improved by 0.27% and 0.89%, respectively, compared with the detection results of the best SAR image rotating object detection model.

Different from previous literature assuming either single reconfigurable intelligent surface (RIS) or multiple RISs at given locations, this letter first studies a multi-user millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system aided by multiple RISs following Poisson point processes. Then we derive an accurate closed-form expression for the spectral efficiency of an arbitrary user and an approximation for the ergodic spectral efficiency of the cell. Moreover, we consider the hardware impairment and model a practical phase-dependent amplitude for each reflecting element. Finally, the simulation results verify the derivations and demonstrate that RISs can provide greater performance gain than the active nodes and require different deployment strategy, which is related to the number of RISs.