The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

In the face of explosive growth of wireless data and electronic devices, cognitive radio (CR) technology is an effective solution to identify spectrum resources underutilized by the primary user (PU). Moreover, cooperative spectrum sensing (CSS) is the key function of CR technology to enhance the detection performance by exploiting spatial diversity via the observations of spatially located sensor nodes (SNs). However, in a classical cooperative paradigm, each SN is always required to report individual local decision about the PU status to the fusion center (FC) for the global decision-making. Such a static reporting model regardless of the surrounding environment is not conducive to achieving the cooperative gain and adapting to a large cognitive wireless sensor network (CWSN). In view of this, this paper proposes an efficient CSS to optimize the reporting time and the achievable throughput of CWSNs, in which consists of selective reporting, multi-bit quantization, data fusion, and dynamic parameter. First of all, SNs only report some necessary observations by means of selective reporting, and then encodes local observations into multi-bit binary codes and send them to the FC. Further, the received local observations are aggregated into a final decision about the PU status. To be specific, the bit number of binary data and the selective reporting range as a parameter are dynamically adjusted at each SN. At last, simulation results demonstrate that the proposed CSS algorithm significantly not only reduces reporting time, but also improves the achievable throughput of CWSNs while maintaining the CSS performance.

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

With the flourishing development of wireless communication, further challenges will be introduced by the future demands of emerging applications. However, in the face of more complex communication scenarios, favorable decoding results may not be yielded by conventional channel decoding schemes based on mathematical models. The remarkable contributions of deep neural networks (DNNs) in various fields have garnered widespread recognition, which has ignited our enthusiasm for their application in wireless communication systems. Therefore, a reliable DNN-based decoding scheme designed for wireless communication systems is proposed. This scheme comprises efficient local decoding using linear and nonlinear operations. To be specific, linear operations are carried out on the edges connecting neurons, while nonlinear operations are performed on each neuron. After forward propagation through the DNN, the loss value is estimated based on the output, and backward propagation is employed to update the weights and biases. This process is performed iteratively until a near-optimal message sequence is recovered. Various factors within the DNN are considered in the simulation and the potential impacts of each factor are analyzed. Simulation results indicate that our proposed DNN-based decoding scheme is superior to the conventional hard decision.

Polar codes can achieve near-capacity performance under low-complexity successive cancellation (SC) decoding. However, short to medium-length block codes exhibit insufficient error-correction performance with the SC algorithm, which limits their practical applications. Although the successive cancellation flip (SCF) decoder improves SC performance, its variable execution time poses challenges in certain scenarios. The simplified restart mechanism (SRM) reduces the execution time of SCF decoders through conditional restart of the additional trials from the second half of the codeword. However, regardless of the position of the flipped bit on the RHS, additional decoding trials can only start from a fixed restart position, which limits efficiency improvements. This paper proposes a flexible restart mechanism (FRM), introducing multiple optional restart positions to further optimize the simplified restart mechanism, reducing both execution time and its variability. In simulations, we used P(1024, 128) and P(2048, 256) polar codes to compare the performance of SCF decoders embedding with FRM and SRM. The results indicate that, compared to the SCF decoder, the SCF decoder with FRM reduces average execution time, average additional execution time, execution time variance, and median additional execution time by 14.95%, 63.42%, 86.55%, and 58.20%, respectively. Compared to the SCF decoder embedding with SRM, FRM achieves improvements in these metrics by 5.15%, 23.14%, 20.24%, and 29.71%, respectively.

This letter studies the joint transmit (tx) power allocation and receive (rx) power splitting scheme for simultaneous wireless information and power transfer (SWIPT) systems. To solve the battery depletion problem, the receivers’ battery status information (BSI) is proposed to be taken into account in our scheme. With the BSI, constraints on the feasible maximum received data rate and harvested energy at receivers are newly induced. The proposed scheme is formulated as an optimization problem to maximize the sum-rate with the rate/energy requirement constraints and the newly induced constraints. To solve the optimization problem, the receivers are firstly classified according to their battery levels, then the optimal solutions are derived by the proposed alternative optimization technique-based algorithm. Simulation results illustrate that the proposed scheme can prolong the battery lifetime and avoid the receivers being to power off.