Journals
  Publication Years
  Keywords
Search within results Open Search
Please wait a minute...
For Selected: Toggle Thumbnails
A Machine Learning-Based Channel Data Enhancement Platform for Digital Twin Channels
AI Bo, ZHANG Yuxin, YANG Mi, HE Ruisi, GUO Rongge
ZTE Communications    2025, 23 (2): 20-30.   DOI: 10.12142/ZTECOM.202502004
Abstract32)   HTML2)    PDF (2951KB)(8)       Save

Reliable channel data helps characterize the limitations and performance boundaries of communication technologies accurately. However, channel measurement is highly costly and time-consuming, and taking actual measurement as the only channel data source may reduce efficiency because of the constraints of high testing difficulty and limited data volume. Although existing standard channel models can generate channel data, their authenticity and diversity cannot be guaranteed. To address this, we use deep learning methods to learn the attributes of limited measured data and propose a generative model based on generative adversarial networks to rapidly synthesize data. A software simulation platform is also established to verify that the proposed model can generate data that are statistically similar to the measured data while maintaining necessary randomness. The proposed algorithm and platform can be applied to channel data enhancement and serve channel modeling and algorithm evaluation applications with urgent needs for data.

Table and Figures | Reference | Related Articles | Metrics
Measurement-Based Channel Characterization for 5G Wireless Communications on Campus Scenario
YANG Mi, HE Ruisi, AI Bo, XIONG Lei, DONG Honghui, LI Jianzhi, WANG Wei, FAN Wei, QIN Hongfeng
ZTE Communications    2017, 15 (1): 8-13.   DOI: 10.3969/j.issn.1673-5188.2017.01.002
Abstract189)   HTML2)    PDF (548KB)(239)       Save

The fifth generation (5G) communication has been a hotspot of research in recent years, and both research institutions and industrial enterprises put a lot of interests in 5G communications at some new frequency bands. In this paper, we investigate the radio channels of 5G systems below 6 GHz according to the 5G communication requirements and scenarios. Channel measurements were conducted on the campus of Beijing Jiaotong University, China at two key optional frequency bands below 6 GHz. By using the measured data, we analyzed key channel parameters at 460 MHz and 3.5 GHz, such as power delay profile, path loss exponent, shadow fading, and delay spread. The results are helpful for the 5G communication system design.

Table and Figures | Reference | Related Articles | Metrics
Millimeter Wave and THz Propagation Channel Modeling for High-Data Rate Railway Connectivity—Status and Open Challenges
Thomas Kürner, GUAN Ke, Andreas F. Molisch, AI Bo, HE Ruisi, LI Guangkai, TIAN Li, DOU Jianwu,and ZHONG Zhangdui
ZTE Communications    2016, 14 (S1): 7-13.   DOI: DOI:10.3969/j.issn.1673-5188.2016.S1.002
Abstract169)      PDF (1577KB)(173)       Save
In the new era of railways, infrastructure, trains and travelers will be interconnected. In order to realize a seamless high-data rate wireless connectivity, up to dozens of GHz bandwidth is required. This motivates the exploration of the underutilized millimeter wave (mmWave) as well as the largely unexplored THz band. In this paper, we first identify relevant communication scenarios for railway applications. Then the specific challenges and estimates of the bandwidth requirements for high-data rate railway connectivity in these communication scenarios are described. Finally, we outline the major challenges on propagation channel modeling and provide a technical route for further studies.
Related Articles | Metrics