What is he best known for?
The fields of study he is best known for:
- Computer network
- Telecommunications
- Algorithm
His main research concerns Mobile edge computing, Base station, Computer network, Cellular network and Scheduling.
His study looks at the relationship between Mobile edge computing and topics such as Computation offloading, which overlap with Distributed computing, Algorithm design, Queueing theory and Stochastic optimization.
His work on Wireless expands to the thematically related Distributed computing.
His Base station research focuses on Network architecture and how it connects with Mobile computing and Wireless network.
His Cellular network research incorporates elements of Radio resource management, Electronic engineering, Telecommunications link and Fading.
His research in Throughput intersects with topics in MIMO and Communication channel.
His most cited work include:
- A Survey on Mobile Edge Computing: The Communication Perspective (1569 citations)
- Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (628 citations)
- Query processing in spatial network databases (575 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Wireless network, Computer network, Distributed computing, Communication channel and Base station.
The Wireless network study combines topics in areas such as Quality of service, Beamforming, Efficient energy use and Artificial intelligence.
His research investigates the link between Computer network and topics such as Relay that cross with problems in Energy harvesting.
His biological study spans a wide range of topics, including Computation offloading, Server, Mobile edge computing and Cache.
Jun Zhang usually deals with Mobile edge computing and limits it to topics linked to Scheduling and Real-time computing.
Jun Zhang combines subjects such as Network architecture, Cellular network and Mobile broadband with his study of Base station.
He most often published in these fields:
- Wireless network (25.19%)
- Computer network (24.44%)
- Distributed computing (21.85%)
What were the highlights of his more recent work (between 2019-2021)?
- Enhanced Data Rates for GSM Evolution (9.63%)
- Artificial intelligence (9.63%)
- Distributed computing (21.85%)
In recent papers he was focusing on the following fields of study:
Enhanced Data Rates for GSM Evolution, Artificial intelligence, Distributed computing, Edge device and Server are his primary areas of study.
His Enhanced Data Rates for GSM Evolution research is multidisciplinary, incorporating elements of Wireless, Convergence and Benchmark.
Within one scientific family, Jun Zhang focuses on topics pertaining to Computer network under Wireless, and may sometimes address concerns connected to Digital signal processing and Information system.
His Artificial intelligence research includes elements of Wireless network, Machine learning and Speedup.
His work in Distributed computing tackles topics such as Overhead which are related to areas like Feature.
His studies in Server integrate themes in fields like Network topology, Load balancing, Computation offloading and Cache.
Between 2019 and 2021, his most popular works were:
- Toward an Intelligent Edge: Wireless Communication Meets Machine Learning (140 citations)
- Mobile Edge Intelligence and Computing for the Internet of Vehicles (49 citations)
- Client-Edge-Cloud Hierarchical Federated Learning (44 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Telecommunications
The scientist’s investigation covers issues in Artificial intelligence, Enhanced Data Rates for GSM Evolution, Server, Edge device and Wireless network.
As a part of the same scientific family, Jun Zhang mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Mobile edge computing and Resource allocation.
His Enhanced Data Rates for GSM Evolution research is multidisciplinary, relying on both Wireless and Overhead.
His study in Server is interdisciplinary in nature, drawing from both Data modeling and Distributed computing.
His work on Distributed computing is being expanded to include thematically relevant topics such as Base station.
His research integrates issues of Transfer of learning, Electronic engineering, Beamforming and Speedup in his study of Wireless network.
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