179 Papers
407 Citations
Hongwen Yang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & MIMO. The author has an hindex of 11, co-authored 140 publications. Previous affiliations of Hongwen Yang include Guangdong University of Technology.
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Papers
Ergodic H-S/MRC Mutual Information
Zeliang Ou,Chongjun Ouyang,Pei Yang,Lu Zhang,Sheng Wu,Hongwen Yang +5 more
- 01 Aug 2019
TL;DR: This paper studies the ergodic mutual information of hybrid selection/maximal-ratio combining (H-S/MRC) diversity system under BPSK/QPSK modulations with simulations to demonstrate the feasibility and validity of the derived results.
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An estimated QoE model for video telephone service
Zhe Wang,Yitong Liu,Yuchen Li,Hongwen Yang,Dacheng Yang +4 more
- 01 Sep 2016
TL;DR: A QoE model, named as Video-Telephone-Mean Opinion Score model, directly focuses on end-users' feeling, and the key performance indicators (KPIs) are mapped toQoE score.
1
Low Density Superposition Modulation using DCT for 5G NOMA scheme
Kun Lu,Sheng Wu,Lihong Lv,Hongwen Yang +3 more
- 09 Aug 2020
TL;DR: A Low Density Superposition Modulation (LDSM) using discrete-cosine transform (DCT) scheme with 5G-NR low-density parity-check (LDPC) channel code is proposed for the 5G scenario and has the 3–5 dB peak-to-average power ratio (PAPR) reduction as compared with the conventional sparse codemultiple access (SCMA) and pattern division multiple access (PDMA).
1
Distortion-aware Virtual Environments Transmission Scheme over a Bandwidth-limited Network
Jingfeng Guo,Yitong Liu,Lin Sang,Hongwen Yang +3 more
- 01 Aug 2019
TL;DR: The main contributions of the proposed scheme include improving the traditional interest management algorithm, so that the method can fit the user’s visual and behavioral characteristics better and proposing a scene distortion estimation algorithm, by which it can effectively calculate and evaluate the scene distortion.
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Mobile User Trajectory Prediction Based on Machine Learning
Ya Liu,Hongwen Yang,Rui Huang +2 more
- 01 Jun 2022
TL;DR: In this paper , the authors proposed an LSTM model that can predict a mobile user's next location based on his historical trajectory and which cell he should connect to at the next moment.
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