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Institution

Beijing University of Posts and Telecommunications

EducationBeijing, 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
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Journal ArticleDOI
03 Jul 2017
TL;DR: A VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks, is constructed and can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space.
Abstract: In recent years, IoV (Internet of Vehicles) has become one of the most active research fields in network and intelligent transportation system. As an open converged network, IoV plays an important role in solving various driving and traffic problems by advanced information and communications technology. We review the existing notions of IoV from different perspectives. Then, we provide our notion from a network point of view and propose a novel IoV architecture with four layers. Particularly, a novel layer named coordinative computing control layer is separated from the application layer. The novel layer is used for solving the coordinative computing and control problems for human-vehicle-environment. After summarizing the key technologies in IoV architecture, we construct a VV (Virtual Vehicle), which is an integrated image of driver and vehicle in networks. VVs can interact with each other in cyber space by providing traffic service and sharing sensing data coordinately, which can solve the communication bottleneck in physical space. Finally, an extended IoV architecture based on VVs is proposed.

99 citations

Journal ArticleDOI
TL;DR: This paper investigates the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS and proposes two AP design schemes for high signal-to-noise ratio (SNR) condition and low SNR condition, respectively.
Abstract: Hybrid precoding is widely studied in millimetre-wave (mmWave) massive MIMO systems due to low cost as well as low power consumption. In general, there are two kinds of hybrid precoding structures: one is fully connected structure (FCS), where each radio frequency (RF) chain is connected to all antennas, and the other is partially connected structure (PCS), where each RF chain is connected to a sub-array. In this paper, we investigate the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS, since this kind of structure is more practical for antenna deployment. We first focus on the optimization of analog precoder (AP) and propose two AP design schemes for high signal-to-noise ratio (SNR) condition and low SNR condition, respectively. For each of the schemes, the original optimization problem is reformulated to single-stream optimal transmitter beamforming problem with per-antenna power constraint, which has an optimal solution. Then, the optimal digital precoder is obtained by water-filling algorithm after AP is determined. Moreover, upper bounds of the achievable data rate for the proposed schemes with closed-form expression are derived.

99 citations

Journal ArticleDOI
TL;DR: A method for estimating systolic and diastolic BP based only on a PPG signal is developed, using the multitaper method (MTM) for feature extraction, and an artificial neural network (ANN) for estimation.
Abstract: The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

99 citations

Proceedings ArticleDOI
01 Mar 2007
TL;DR: An improved branch and bound algorithm which is more efficient than the general branch and Bound algorithm is proposed for optimal power control optimization problem in cognitive radio network.
Abstract: In cognitive radio network, the interference of the unlicensed users to the licensed users should be limited under interference temperature constraints. In this paper, the optimal power control scheme of a network is analyzed without interference temperature constraints firstly. Based on this, considering interference temperature constraints, the optimal power control in cognitive radio network is modeled as a concave minimization problem. Some useful properties of the power control optimization problem are exploited. According to these properties, an improved branch and bound algorithm which is more efficient than the general branch and bound algorithm is proposed for optimal power control optimization problem in cognitive radio network.

99 citations

Journal ArticleDOI
TL;DR: For a suite of 14 benchmark problems, NEP outperforms the improved evolutionary programming using mutation based on Levy probability distribution (ILEP) for multimodal functions with many local minima while being comparable to ILEP in performance for unimodal and multimodals functions with only a few minima.

99 citations


Authors

Showing all 39925 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Jian Li133286387131
Ming Li103166962672
Kang G. Shin9888538572
Lei Liu98204151163
Muhammad Shoaib97133347617
Stan Z. Li9753241793
Qi Tian96103041010
Xiaodong Xu94112250817
Qi-Kun Xue8458930908
Long Wang8483530926
Jing Zhou8453337101
Hao Yu8198127765
Mohsen Guizani79111031282
Muhammad Iqbal7796123821
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202394
2022533
20213,009
20203,720
20193,817
20183,296