What is he best known for?
The fields of study he is best known for:
- Telecommunications
- Computer network
- Statistics
His scientific interests lie mostly in MIMO, Base station, Telecommunications link, Communications system and Transmitter power output.
His MIMO study deals with the bigger picture of Communication channel.
Jakob Hoydis interconnects Cellular network and Array gain in the investigation of issues within Communication channel.
His Base station research focuses on Wireless and how it connects with Radio wave.
His studies in Communications system integrate themes in fields like Artificial neural network, Deep learning and Artificial intelligence.
Jakob Hoydis studied Transmitter power output and Control theory that intersect with Matched filter, Many antennas, Path loss and 3G MIMO.
His most cited work include:
- Massive MIMO in the UL/DL of Cellular Networks: How Many Antennas Do We Need? (1945 citations)
- An Introduction to Deep Learning for the Physical Layer (990 citations)
- Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come (573 citations)
What are the main themes of his work throughout his whole career to date?
Jakob Hoydis mainly investigates Communication channel, MIMO, Artificial neural network, Telecommunications link and Fading.
His research in the fields of Additive white Gaussian noise overlaps with other disciplines such as Random matrix.
His research in MIMO intersects with topics in Channel state information, Spectral efficiency, Base station and Topology.
His Base station study incorporates themes from Path loss, Key, Transmitter power output and Signal processing.
His Artificial neural network research is multidisciplinary, relying on both Testbed, Deep learning, Computer engineering and Communications system.
His study on Telecommunications link also encompasses disciplines like
- Multi-user MIMO that intertwine with fields like 3G MIMO,
- Cellular network which connect with Spatial multiplexing.
He most often published in these fields:
- Communication channel (52.94%)
- MIMO (44.54%)
- Artificial neural network (21.85%)
What were the highlights of his more recent work (between 2019-2021)?
- Communication channel (52.94%)
- MIMO (44.54%)
- Wireless (13.45%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Communication channel, MIMO, Wireless, Transmitter and Artificial intelligence.
His Communication channel research is multidisciplinary, incorporating perspectives in Algorithm and Computer engineering.
His Computer engineering research is multidisciplinary, incorporating elements of Artificial neural network, Decoding methods and Communications system.
His research in MIMO intersects with topics in Spatial correlation, Detector, Computer architecture and Air interface.
As part of one scientific family, Jakob Hoydis deals mainly with the area of Spatial correlation, narrowing it down to issues related to the Key, and often Base station.
The Transmitter study combines topics in areas such as Computer hardware and Electronic engineering.
Between 2019 and 2021, his most popular works were:
- Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination (74 citations)
- Adaptive Neural Signal Detection for Massive MIMO (28 citations)
- Trainable Communication Systems: Concepts and Prototype (18 citations)
In his most recent research, the most cited papers focused on:
- Telecommunications
- Computer network
- Algorithm
His primary scientific interests are in Communication channel, MIMO, Computer engineering, Wireless and Spatial correlation.
His research integrates issues of Detector, Artificial neural network, Base station, Signal processing and Transmitter in his study of Communication channel.
The concepts of his Artificial neural network study are interwoven with issues in Decoding methods, Low-density parity-check code, Code and Communications system.
His study in the field of User equipment also crosses realms of Mobile telephony.
His Wireless study combines topics from a wide range of disciplines, such as Protocol stack and Computer network.
His Spatial correlation research integrates issues from Minimum mean square error, Algorithm and Detection theory.
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