What is he best known for?
The fields of study he is best known for:
- Computer network
- Telecommunications
- Statistics
His primary areas of study are Computer network, Wireless, Communication channel, Efficient energy use and Resource allocation.
His biological study spans a wide range of topics, including Cognitive radio and Throughput.
His Wireless study typically links adjacent topics like Transmission.
Geoffrey Ye Li has included themes like Algorithm and Deep learning, Artificial intelligence in his Communication channel study.
The various areas that Geoffrey Ye Li examines in his Efficient energy use study include Energy conservation, Energy consumption, Telecommunications, Spectral efficiency and Electronic engineering.
His biological study deals with issues like Radio resource management, which deal with fields such as Heterogeneous network.
His most cited work include:
- An Overview of Massive MIMO: Benefits and Challenges (1455 citations)
- Fundamental trade-offs on green wireless networks (983 citations)
- Cognitive radio networking and communications: an overview (784 citations)
What are the main themes of his work throughout his whole career to date?
Communication channel, Computer network, Wireless, MIMO and Electronic engineering are his primary areas of study.
His Communication channel research includes themes of Deep learning, Artificial intelligence and Communications system.
His Computer network research is multidisciplinary, incorporating perspectives in Cognitive radio and Throughput.
Geoffrey Ye Li studies Wireless, namely Wireless network.
He has researched MIMO in several fields, including Algorithm and Telecommunications link.
His research integrates issues of Telecommunications, Transmission and Antenna in his study of Electronic engineering.
He most often published in these fields:
- Communication channel (31.78%)
- Computer network (30.57%)
- Wireless (25.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Communication channel (31.78%)
- Artificial intelligence (14.98%)
- MIMO (24.90%)
In recent papers he was focusing on the following fields of study:
Geoffrey Ye Li mostly deals with Communication channel, Artificial intelligence, MIMO, Deep learning and Wireless.
His studies deal with areas such as Electronic engineering and Overhead as well as Communication channel.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Spectral efficiency.
His research investigates the connection between MIMO and topics such as Base station that intersect with problems in Quantization, Relay and Throughput.
His study focuses on the intersection of Wireless and fields such as Transmitter with connections in the field of Transmission.
His work focuses on many connections between Radio spectrum and other disciplines, such as Radio frequency, that overlap with his field of interest in Computer network.
Between 2019 and 2021, his most popular works were:
- Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks (53 citations)
- Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities (50 citations)
- Model-Driven Deep Learning for MIMO Detection (47 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Telecommunications
- Statistics
Wireless, Artificial intelligence, MIMO, Wireless network and Communication channel are his primary areas of study.
His Wireless research is under the purview of Telecommunications.
Many of his research projects under Artificial intelligence are closely connected to Resource management with Resource management, tying the diverse disciplines of science together.
His MIMO study incorporates themes from Base station, Signal processing, Multiplexing, Telecommunications link and Compressed sensing.
His Wireless network study combines topics in areas such as Resource allocation, Multimedia, Federated learning, Cognitive radio and Key.
His study in Communication channel is interdisciplinary in nature, drawing from both Algorithm and Overhead.
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.
