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.

Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities

Abstract: With rapid development of compelling application scenarios of the Internet of Things (IoT), such as smart cities, it becomes substantially important to strengthen the management of data traffic in IoT networks. Traffic classification is beneficial in terms of both ensuring network security and improving quality of service. Traditional IoT traffic classification methods separate the classification algorithm and the design of feature engineering, which includes feature extraction and feature selection. Then, traffic identification or classification is performed by combining both. This paper proposes an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios. To the best of our knowledge, this is the first time that capsule networks have been used in the context of traffic classification. Experimental results show the feasibility and effectiveness of our proposed traffic classification mechanism, which yields high classification accuracy.

Text sentiment analysis based on long short-term memory

Abstract: With the rapid development of Internet and big explosion of text data, it has been a very significant research subject to extract valuable information from text ocean. To realize multi-classification for text sentiment, this paper promotes a RNN language model based on Long Short Term Memory (LSTM), which can get complete sequence information effectively. Compared with the traditional RNN language model, LSTM is better in analyzing emotion of long sentences. And as a language model, LSTM is applied to achieve multi-classification for text emotional attributes. So though training different emotion models, we can know which emotion the sentence belongs to by using these emotion models. And numerical experiments show that it can produce better accuracy rate and recall rate than the conventional RNN.

Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network

Abstract: In existing works of deep learning-based resource allocation, the scalability degrades heavily with the increase of network complexity, which is due to their limited learning ability of shallow neural networks and insufficient knowledge of network. Nowadays, to address the growth of cell density, cooperative beamforming in user-centric network (UCN) is emerged, where the additional degrees of freedom of multi-antenna and cell coordination aggravate the challenges. This letter proposes a deep residual learning framework, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information. Specifically, a weighted minimum mean square error (WMMSE)-based algorithm is derived to determine the optimal clusters and beamforming matrices; then UcnBeamNet is trained to learn the input-output mapping and provide direct insight of UCN from association matrices in addition to plural inputs. Extensive experiments demonstrate UcnBeamNet still reaches 90.38% sum-rate relative to conventional algorithm even with a large network size, and achieves more than 50, $000\times $ speed up in computational efficiency.

A Computation Offloading Algorithm Based on Game Theory for Vehicular Edge Networks

Abstract: Mobile Edge Computing (MEC) offers a new paradigm to improve vehicular services and augment the capabilities of vehicles. In this paper, to reduce the latency of the computation offloading of vehicles, we study multiple vehicles computation offloading problem in vehicular edge networks. We formulate the problem as a multi-user computation offloading game problem, prove the existence of Nash equilibrium (NE) of the game and propose a distributed computation offloading algorithm to compute the equilibrium. We analyze the price of anarchy of the game algorithm and evaluate the performance of the game algorithm using extensive simulations. Numerical results show that the proposed algorithm can greatly reduce the computation overhead of vehicles.