Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching

Abstract: In the area of full duplex (FD) enabled small cell networks (SCNs), only limited works have been done on the consideration of mobile edge computing (MEC) and caching. In this paper, a virtual FD-enabled SCN framework with MEC and caching is investigated for two kinds of heterogeneous services, high-data-rate service and computation-sensitive service. In our proposed framework, content caching and FD communication are jointly considered to provide high-data-rate service without the cost of backhaul resource. And computation-sensitive service is offloaded to MEC, guaranteeing the delay requirement of users. From the view point of heterogeneous services, we formulate a virtual resource allocation problem, in which quality of experience of users and corresponding resource consumption are recognized as system revenue and cost, respectively. Particularly, user association, power control and resources (including spectrum, caching, and computing) allocation are jointly considered. Since the optimized problem is nonconvex, necessary variable relaxation and reformulation are conducted to transfer the original problem to a convex problem. Furthermore, alternating direction method of multipliers algorithm is adopted to obtain the optimal solution with low computation complexity. Finally, extensive simulations are conducted with different system parameter configurations to verify the effectiveness of our proposed scheme.

Analysis and Simulation for Spectrum Aggregation in LTE-Advanced System

Abstract: Spectrum aggregation (SA) is one of the potential LTE advanced technologies. The analysis and simulation for SA in LTE-Advanced system are addressed in this paper. We first analyze the system model of SA, and propose some evaluation methodologies for SA. Then the schedulers for SA with joint queue and disjoint queue are proposed. Finally, different traffic models are considered in the system level simulation to evaluate the performance gain of spectrum aggregation over deploying independent carriers on the eNode-B. The simulation results show that the SA technology can enhance the throughput, fairness and latency performance in LTE-Advanced system comparing with independent carrier scenario for different traffic models.

Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks

Abstract: Human action recognition is one of the fundamental challenges in robotics systems. In this paper, we propose one lightweight action recognition architecture based on deep neural networks just using RGB data. The proposed architecture consists of convolution neural network (CNN), long short-term memory (LSTM) units, and temporal-wise attention model. First, the CNN is used to extract spatial features to distinguish objects from the background with both local and semantic characteristics. Second, two kinds of LSTM networks are performed on the spatial feature maps of different CNN layers (pooling layer and fully-connected layer) to extract temporal motion features. Then, one temporal-wise attention model is designed after the LSTM to learn which parts in which frames are more important. Lastly, a joint optimization module is designed to explore intrinsic relations between two kinds of LSTM features. Experimental results demonstrate the efficiency of the proposed method.