Institution
Beijing University of Posts and Telecommunications
Education•Beijing, Beijing, China•
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.
Papers published on a yearly basis
Papers
More filters
•
TL;DR: In this article, the authors review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios, including NOMA, MIMO, and millimeter wave.
Abstract: The new demands for high-reliability and ultra-high capacity wireless communication have led to extensive research into 5G communications. However, the current communication systems, which were designed on the basis of conventional communication theories, signficantly restrict further performance improvements and lead to severe limitations. Recently, the emerging deep learning techniques have been recognized as a promising tool for handling the complicated communication systems, and their potential for optimizing wireless communications has been demonstrated. In this article, we first review the development of deep learning solutions for 5G communication, and then propose efficient schemes for deep learning-based 5G scenarios. Specifically, the key ideas for several important deep learningbased communication methods are presented along with the research opportunities and challenges. In particular, novel communication frameworks of non-orthogonal multiple access (NOMA), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are investigated, and their superior performances are demonstrated. We vision that the appealing deep learning-based wireless physical layer frameworks will bring a new direction in communication theories and that this work will move us forward along this road.
93 citations
••
TL;DR: A routing algorithm called minimum fusion Steiner tree (MFST) for energy efficient data gathering with aggregation (fusion) in wireless sensor networks that incorporates the cost for data fusion, which can be significant for emerging sensor networks with vectorial data and/or security requirements.
Abstract: In this paper, we propose a routing algorithm called minimum fusion Steiner tree (MFST) for energy efficient data gathering with aggregation (fusion) in wireless sensor networks. Different from existing schemes, MFST not only optimizes over the data transmission cost, but also incorporates the cost for data fusion, which can be significant for emerging sensor networks with vectorial data and/or security requirements. By employing a randomized algorithm that allows fusion points to be chosen according to the nodes' data amounts, MFST achieves an approximation ratio of 5/4log(k + 1), where k denotes the number of source nodes, to the optimal solution for extremely general system setups, provided that fusion cost and data aggregation are nondecreasing against the total input data. Consequently, in contrast to algorithms that only excel in full or nonaggregation scenarios without considering fusion cost, MFST can thrive in a wide range of applications
93 citations
••
TL;DR: Some basic relations among system bandwidth, threshold SIR, maximum number of access users, as well as system spectral efficiency are studied for both traditional CDMA and large area synchronous CDMA (LAS-CDMA).
Abstract: Some basic relations among system bandwidth, threshold SIR, maximum number of access users, as well as system spectral efficiency are studied for both traditional CDMA and large area synchronous CDMA (LAS-CDMA). The perspectives of LAS-CDMA for the 4G are also studied.
93 citations
••
TL;DR: A coupled fourth-order nonlinear Schrodinger system, which describes the ultrashort optical pluses in a birefringent optical fiber, is investigated, and two- and three-soliton solutions are derived.
93 citations
••
01 Jul 2020
TL;DR: This paper model the user-news interactions as a bipartite graph and proposes a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD, which can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.
Abstract: With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user’s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.
93 citations
Authors
Showing all 39925 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jie Zhang | 178 | 4857 | 221720 |
Jian Li | 133 | 2863 | 87131 |
Ming Li | 103 | 1669 | 62672 |
Kang G. Shin | 98 | 885 | 38572 |
Lei Liu | 98 | 2041 | 51163 |
Muhammad Shoaib | 97 | 1333 | 47617 |
Stan Z. Li | 97 | 532 | 41793 |
Qi Tian | 96 | 1030 | 41010 |
Xiaodong Xu | 94 | 1122 | 50817 |
Qi-Kun Xue | 84 | 589 | 30908 |
Long Wang | 84 | 835 | 30926 |
Jing Zhou | 84 | 533 | 37101 |
Hao Yu | 81 | 981 | 27765 |
Mohsen Guizani | 79 | 1110 | 31282 |
Muhammad Iqbal | 77 | 961 | 23821 |