What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Statistics
His main research concerns Computer network, Cellular network, Distributed computing, Mobile broadband and Reuse.
His studies in Computer network integrate themes in fields like Wireless, Wireless network and Physical layer.
His Cellular network research is multidisciplinary, incorporating elements of Systems design, Resource allocation, Mobile computing and Component.
His Resource allocation study integrates concerns from other disciplines, such as Vehicular communication systems and Cloud computing, Mobile cloud computing.
Depeng Jin interconnects Simulated annealing, Routing, Markov process, Next-generation network and Game theory in the investigation of issues within Distributed computing.
The Mobile broadband study which covers Heterogeneous network that intersects with Quality of service, Interconnection, Radio access network and Full virtualization.
His most cited work include:
- A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges (635 citations)
- Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures (484 citations)
- Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey (206 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computer network, Distributed computing, Cellular network, Throughput and Wireless.
The various areas that he examines in his Computer network study include Transmission and Mobile broadband.
His Distributed computing study which covers Markov process that intersects with Markov chain.
His Cellular network research is multidisciplinary, incorporating perspectives in Resource allocation and Base station.
Depeng Jin is involved in the study of Wireless that focuses on Wireless network in particular.
While working on this project, Depeng Jin studies both Scheduling and Reuse.
He most often published in these fields:
- Computer network (48.94%)
- Distributed computing (21.16%)
- Cellular network (11.90%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (6.88%)
- Computer network (48.94%)
- Machine learning (4.76%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer network, Machine learning, Cellular network and Data mining.
His work in Network packet, Queue, Cache, Radio access network and Network element is related to Computer network.
His Machine learning study combines topics from a wide range of disciplines, such as Matching, Trajectory and Hidden Markov model.
His research in Cellular network intersects with topics in Computer security, Resource allocation, Mobile computing and Base station.
His Resource allocation research includes themes of Wireless, Underlay and Secure transmission.
The concepts of his Mobile computing study are interwoven with issues in World Wide Web and Social network.
Between 2017 and 2021, his most popular works were:
- DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (127 citations)
- A Survey on Socially Aware Device-to-Device Communications (43 citations)
- DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis (40 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Statistics
Depeng Jin mainly focuses on Cellular network, Artificial intelligence, Machine learning, Data mining and Deep learning.
Cellular network is a subfield of Computer network that Depeng Jin tackles.
Depeng Jin combines subjects such as Sampling and Recommender system with his study of Artificial intelligence.
His Machine learning research is multidisciplinary, relying on both Trajectory, Hidden Markov model and Graph.
His Data mining study combines topics in areas such as Airfield traffic pattern, Structure and Benchmark.
His research integrates issues of Domain, Code, Identity, Linkage and Business intelligence in his study of Deep learning.
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