Driven by the explosive data traffic and new quality of service (QoS) requirement of mobile users, the communication industry has been experiencing a new evolution by means of network infrastructure densification. With the increase of the density as well as the variety of access points (APs), the network benefits from proximal transmissions and increased spatial reuse of system resources, thus introducing a new paradigm named ultra-dense networks (UDNs). Since the limited available resources are shared by ubiquitous APs in UDNs, the demand for efficient resource allocation schemes becomes even more compelling. However, the large scale of UDNs impedes the exploration of effective resource allocation approaches particularly on the computational complexity and significance overhead or feedback. In this paper, we provide a survey-style introduction to resource allocation approaches in UDNs. Specifically, we first present some common scenarios of UDNs with the relevant special issues. Second, we provide a taxonomy to classify the resource allocation methods in the existing literatures. Then, to alleviate the main difficulties of UDNs, some prevailing and feasible solutions are elaborated. Next, we present some emerging technologies thriving UDNs with special RA features discussed. Additionally, the challenges and open research directions are outlined in this field.

Traditional human activity recognition (HAR) based on motion sensor adopts sliding window labeling and prediction. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, we propose a novel HAR method based on U-Net to overcome the multi-class problem, performing activity labeling and prediction of each sampling point. The motion sensor data collected from the wearable sensors is mapped into an image with the single pixel column and multi-channel, then it is input into the U-Net network to complete the pixel-level activity recognition function. We design a complete HAR framework based on U-Net to realize the dense prediction of motion sensor data, including data preprocessing, dense prediction and post analysis. In order to further improve the dense prediction performance, we propose the post-correction algorithm for the dense prediction results on the basis of the activity misalignment analysis. The extensive experimental results demonstrate that our U-Net method performs better than the traditional machine learning and deep learning methods based on the sliding window prediction. And it also outperforms Full Convolutional Network (FCN), SegNet and Mask R-CNN based on the dense prediction on the four datasets. Moreover, it also shows the better robustness and excellent performance of recognition on the short-term activities and minority classes. We release a new dataset named Sanitation which includes seven types of daily work activity data of sanitation workers to evaluate the HAR algorithm’s performance.

Network attack behavior detection using deep learning is an important research topic in the field of network security. Currently, there are still many challenges in detecting multi-class imbalanced abnormal traffic data. This paper proposed a new intrusion detection network based on deep learning, named parallel cross convolutional neural network (PCCN), to improve the detection performance of imbalanced abnormal flows. By fusing the flow features learned from the two branch convolutional neural networks (CNN), PCCN can better learn the flow features with fewer samples, to improve the detection results of the imbalanced abnormal flows. We proposed an improved feature extraction method of the original flow to extract multi-class flow features at the same time. The proposed algorithm not only reduces the number of useless elements for network learning, but also accelerates network convergence. In addition, we proposed four improved versions of the PCCN network structure to meet the real-time requirements of network intrusion detection in the current big data computing. These networks can achieve almost the same detection results as the PCCN, but greatly reduce the detection time of data. Through the analysis of high-order evaluation metrics, the proposed PCCN algorithm is significantly better than the traditional machine learning algorithms. Compared with the current hierarchical network model, PCCN can also achieve better performance in term of overall accuracy.

This paper proposes a method for vehicle driving behavior recognition (VDBR) based on a six-axis motion processor. This method uses deep learning technology to learn the sample data collected by the on-board sensor. To solve the problem of small sample size and easy overfitting, we propose a joint data augmentation (JDA) scheme, and design a new multi-view convolutional neural network model (MV-CNN). The JDA includes the multi-axis weighted fusion (MAWF) algorithm, background noise fusion (BNF) algorithm and random cropping (RC) algorithm to construct a sample dataset that is more in line with a complex real driving environment. With the CNN model, the direction of information propagation improves, and a new MV-CNN model was developed for the training, learning and recognition of driving behavior. The performance of MV-CNN is experimentally compared with CNN, RNN, LSTM, CNN+LSTM and 3D-CNN. The results show that MV-CNN can obtain the best recall, precision, and F1-score. At the same time, MV-CNN and JDA have better generalization ability, reduce the training variance and deviation, and increase the stability of the model training process.