What is he best known for?
The fields of study he is best known for:
- Computer network
- Telecommunications
- Artificial intelligence
Computer network, MIMO, Communication channel, Cognitive radio and Base station are his primary areas of study.
His work carried out in the field of Computer network brings together such families of science as Wireless, Energy consumption, Software-defined radio, Frequency-hopping spread spectrum and Reinforcement learning.
The MIMO study combines topics in areas such as Electronic engineering, Telecommunications link and Control theory.
Huaiyu Dai works mostly in the field of Telecommunications link, limiting it down to topics relating to Transmission and, in certain cases, Noma, Power control and Transmitter power output, as a part of the same area of interest.
The various areas that Huaiyu Dai examines in his Cognitive radio study include Algorithm design, Frequency domain, State and Radio frequency.
His Base station research is multidisciplinary, incorporating perspectives in Real-time computing and Asynchronous communication.
His most cited work include:
- Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks (478 citations)
- A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (330 citations)
- Relay Selection for Cooperative NOMA (325 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer network, Distributed computing, Wireless, MIMO and Electronic engineering.
His research in Computer network intersects with topics in Wireless network, Transmission, Physical layer and Throughput.
Huaiyu Dai combines subjects such as Subgraph isomorphism problem and Network topology with his study of Distributed computing.
His studies deal with areas such as Real-time computing and Base station as well as Wireless.
His MIMO course of study focuses on Decoding methods and Multiplexing.
His Electronic engineering research includes themes of Precoding, Communication channel, Fading and Telecommunications link.
He most often published in these fields:
- Computer network (30.50%)
- Distributed computing (20.13%)
- Wireless (17.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Wireless (17.92%)
- Real-time computing (11.95%)
- Distributed computing (20.13%)
In recent papers he was focusing on the following fields of study:
Huaiyu Dai mostly deals with Wireless, Real-time computing, Distributed computing, Computer network and Base station.
His Wireless research incorporates themes from Optimization problem, MIMO and Robustness.
His MIMO study integrates concerns from other disciplines, such as Algorithm, Decoding methods and Telecommunications link.
His study in Real-time computing is interdisciplinary in nature, drawing from both Data collection, Power control, Relay, Wireless sensor network and Transmission.
His Transmission study combines topics in areas such as Communication channel, User equipment and Precoding.
His biological study spans a wide range of topics, including Wireless network, Physical layer and Reinforcement learning.
Between 2018 and 2021, his most popular works were:
- Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting (65 citations)
- Energy Efficiency of RSMA and NOMA in Cellular-Connected mmWave UAV Networks (36 citations)
- Dynamic Mobility-Aware Interference Avoidance for Aerial Base Stations in Cognitive Radio Networks (27 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Telecommunications
- Artificial intelligence
Huaiyu Dai mainly focuses on Real-time computing, Distributed computing, Wireless, Server and Computer network.
His study in Real-time computing is interdisciplinary in nature, drawing from both Data collection, Metric, Relay, Wireless sensor network and Base station.
Huaiyu Dai interconnects Telecommunications network, Cellular network and Transmission in the investigation of issues within Base station.
His studies deal with areas such as Energy, Electronic engineering and Spectral efficiency as well as Transmission.
His Wireless research is multidisciplinary, incorporating perspectives in MIMO and Precoding.
His Computer network research is multidisciplinary, relying on both Physical layer and Reinforcement learning.
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.
