His scientific interests lie mostly in Computer network, Wireless, Interference alignment, Wireless network and Interference. His research links Throughput with Computer network. His Wireless study integrates concerns from other disciplines, such as Network topology, Distributed computing and Base station.
His Interference alignment research incorporates elements of Energy harvesting and Key. His Wireless network research includes elements of Interference, Signal-to-interference-plus-noise ratio, Zero-forcing precoding, Electronic engineering and Channel state information. His Interference research includes themes of Cognitive radio, Communication channel, Efficient energy use and Transceiver.
Nan Zhao spends much of his time researching Computer network, Wireless, Communication channel, Wireless network and Interference. His study on Eavesdropping, Secure transmission and Base station is often connected to Jamming as part of broader study in Computer network. His studies deal with areas such as Transmitter power output, Energy harvesting, Maximum power transfer theorem, Electronic engineering and Efficient energy use as well as Wireless.
His Communication channel research is multidisciplinary, relying on both Algorithm, Overhead and Relay. His research on Wireless network also deals with topics like
His primary areas of investigation include Computer network, Wireless, Noma, Wireless network and Transmission. His research in Computer network intersects with topics in Relay, Throughput and Transmitter power output. His research integrates issues of Wireless sensor network, Communication channel, Optimization problem, Efficient energy use and Maximum power transfer theorem in his study of Wireless.
The various areas that Nan Zhao examines in his Wireless network study include Node, Upper and lower bounds and Electrical engineering. His study looks at the intersection of Transmission and topics like Spectral efficiency with Computer engineering, Quality of service and Bandwidth. The Eavesdropping study combines topics in areas such as Channel state information and Base station.
Nan Zhao mainly focuses on Wireless, Computer network, Noma, Mathematical optimization and Efficient energy use. His Wireless study incorporates themes from Node, Optimization problem, Wireless sensor network and Communication channel. His work carried out in the field of Computer network brings together such families of science as Throughput and Spectrum sharing.
His Noma research also works with subjects such as
Beamforming, Precoding and Electronic engineering most often made with reference to Eavesdropping,
Secure transmission that intertwine with fields like Interference, Transmitter power output and Upper and lower bounds. His studies in Precoding integrate themes in fields like Wireless network and Single antenna interference cancellation. His Efficient energy use study also includes
Energy harvesting and Power budget most often made with reference to Maximum power transfer theorem,
Cellular network which connect with Real-time computing, Resource allocation, Heuristic and Quality of service.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Survey of Channel Modeling for UAV Communications
Aziz Altaf Khuwaja;Yunfei Chen;Nan Zhao;Mohamed-Slim Alouini.
IEEE Communications Surveys and Tutorials (2018)
Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach
Ying He;Nan Zhao;Hongxi Yin.
IEEE Transactions on Vehicular Technology (2018)
Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
Ying He;F. Richard Yu;Nan Zhao;Victor C. M. Leung.
UAV-Assisted Emergency Networks in Disasters
Nan Zhao;Weidang Lu;Min Sheng;Yunfei Chen.
UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells
Fen Cheng;Shun Zhang;Zan Li;Yunfei Chen.
Caching UAV Assisted Secure Transmission in Hyper-Dense Networks Based on Interference Alignment
Nan Zhao;Fen Cheng;F. Richard Yu;Jie Tang.
Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks
Ying He;Zheng Zhang;F. Richard Yu;Nan Zhao.
Synthesis of hierarchical porous carbons for supercapacitors from coal tar pitch with nano-Fe2O3 as template and activation agent coupled with KOH activation
Xiaojun He;Nan Zhao;Jieshan Qiu;Nan Xiao.
Journal of Materials Chemistry (2013)
Big Data Analytics in Mobile Cellular Networks
Ying He;Fei Richard Yu;Nan Zhao;Hongxi Yin.
IEEE Access (2016)
Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks
Nan Zhao;F. Richard Yu;Hongjian Sun;Ming Li.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: