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The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.

In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless networks. In this overview, we first illustrate the construction method of wireless communication graph for various wireless networks and simply introduce the progress of several classical paradigms of GNNs. Then, several applications of GNNs in wireless networks such as resource allocation and several emerging fields, are discussed in detail. Finally, some research trends about the applications of GNNs in wireless communication systems are discussed.

With the rapid development of the Internet, watermarks are widely used in images to protect copyright. This implies that the robustness of watermark is very important. In recent years, there have been some studies to evaluate watermark performance by removing the watermark. Among them, some methods need to mark the watermark position in advance, and some require multiple images with the same watermark. Moreover, when the colour of thewatermark is similar to that of the background, the existing methods can hardly remove the watermark from the watermarked image. In the proposed work, the authors presented a watermark removal structure consisting of watermark extraction and image inpainting to address the aforementioned issues. In particular, the extraction network is used to extract the watermark in the watermarked image, and the inpainting network is used to inpainting image for a better watermark removal image, respectively. Finally, the authors train and test the developed network architecture by constructing two data sets, i.e. white watermarked image data set (WW‐data set) and colour watermarked image data set (CW‐data set). The proposed method not only has better performance on the WW‐data set than the current latest methods (on the CW‐data set, other methods have almost failed) but also effectively removes the watermarks.

In this paper, we propose a multi-layer mobile application (app) scheduling method to extend the capability of low-end Android devices. With a quantitative analysis, we find that the increase of installed apps will negatively affect quality of experience (QoE) of the user, e.g., the action response time of a mobile device, by producing more periodically or irregularly background tasks. On the other hand, the user tends to install more apps than needed in case they could play a role someday, as indicated by the consumer app usage statistics. When the storage is running out, being forced to choose an app to uninstall due to space budget is a painful experience for the user. This contradiction is intensified for low-end devices due to limited resources. We try to reduce this dilemma by a multi-layer app scheduling (MAS) schema, along with a cloud service. For the first layer, we utilize the “freeze”feature of Android to prevent non-essential background activities. For the second layer, it is a network scheduler, which automatically schedules the available apps, together with their data, between local and cloud according to user's personal policy generated by big data analysis. By dynamically scheduling the apps among three states, QoE of a low-end Android device is improved. At the same time, with the help of an app state recovery mechanism, the user can directly access a large number of apps provided by the cloud with consistent app view. Experimental results on a low-end smartphone, i.e., Samsung Galaxy ON5, and a smart watch based on Newton2_Plus wearable development board show the benefits of the proposed MAS schema.

Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research.

This paper presents a framework for mobile transparent computing. It extends the PC transparent computing to mobile terminals. Since resources contain different kinds of operating systems and user data that are stored in a remote server, how to manage the network resources is essential. In this paper, we apply the technologies of quick emulator (QEMU) virtualization and mobile agent for mobile transparent computing (MTC) to devise a method of managing shared resources and services management (SRSM). It has three layers: a user layer, a manage layer, and a resource layer. A mobile virtual terminal in the user layer and virtual resource management in the manage layer cooperate to maintain the SRSM function accurately according to the user’s requirements. An example of SRSM is used to validate this method. Experiment results show that the strategy is effective and stable.

While many protocols for sensor network security provide confidentiality for message content, contextual information usually remains exposed, which can be critical to the mission of the sensor network. In this paper, we propose an energy-efficient cyclic diversionary routing (CDR) scheme against global eavesdroppers for preserving location privacy and maximizing lifetime of wireless sensor networks (WSNs). To ensure no impact on lifetime of WSNs, we minimize the energy consumption of hotspots and generate abundant diversionary routing paths in areas far from sink. To enhance the location privacy preservation, we theoretically figure out the probability of the cyclic interference routings in different areas of the network and statistically achieve an energy consumption balance in the entire network. Analysis and simulation results show that CDR significantly improves the security in source-location privacy preservation without reducing the network lifetime.