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, Communication channel, Decoding methods, Topology and Fading.
Computer network is closely attributed to Relay in his work.
His Communication channel research is multidisciplinary, relying on both Relay channel and Transmitter.
His Transmitter study combines topics in areas such as Transmission, Real-time computing and Communications system.
His work deals with themes such as MIMO, Multiplexing, Secrecy and Shared secret, which intersect with Topology.
Much of his study explores Fading relationship to Mathematical optimization.
His most cited work include:
- The Multiway Relay Channel (296 citations)
- Designing intelligent energy harvesting communication systems (288 citations)
- Learning-based optimization of cache content in a small cell base station (246 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Communication channel, Computer network, Topology, Algorithm and Transmission.
The study incorporates disciplines such as Transmitter, Decoding methods and Wireless in addition to Communication channel.
His research in Transmitter intersects with topics in Real-time computing and Communications system.
His Computer network research incorporates themes from Relay channel, Relay and Wireless network.
Deniz Gunduz usually deals with Topology and limits it to topics linked to MIMO and Multiplexing and Spectral efficiency.
The various areas that Deniz Gunduz examines in his Fading study include Mathematical optimization and Channel state information.
He most often published in these fields:
- Communication channel (39.19%)
- Computer network (30.08%)
- Topology (17.80%)
What were the highlights of his more recent work (between 2019-2021)?
- Communication channel (39.19%)
- Wireless (15.68%)
- Computer network (30.08%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Communication channel, Wireless, Computer network, Algorithm and Distributed computing.
In his study, Electronic engineering is inextricably linked to Modulation, which falls within the broad field of Communication channel.
Many of his research projects under Wireless are closely connected to Contextual image classification with Contextual image classification, tying the diverse disciplines of science together.
His biological study spans a wide range of topics, including Adversarial system and Federated learning.
Deniz Gunduz has researched Distributed computing in several fields, including Energy consumption, Energy harvesting, Markov decision process and Reinforcement learning.
His study explores the link between Fading and topics such as Analog transmission that cross with problems in Decoding methods and Broadband.
Between 2019 and 2021, his most popular works were:
- Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air (141 citations)
- Federated Learning Over Wireless Fading Channels (113 citations)
- Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks (55 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Statistics
Deniz Gunduz mainly focuses on Wireless, Communication channel, Computer network, Edge device and Fading.
His work in the fields of Wireless, such as Wireless network and Channel state information, overlaps with other areas such as Perspective.
In his study, which falls under the umbrella issue of Wireless network, Computer engineering, Throughput and Efficient energy use is strongly linked to Transmission.
His Communication channel study also includes fields such as
- Bandwidth and related Channel code,
- Modulation, which have a strong connection to Electronic engineering.
His Computer network research is multidisciplinary, incorporating elements of Independent and identically distributed random variables and Federated learning.
In his study, Decoding methods, Convergence, Transmitter power output, Broadband and Bottleneck is strongly linked to Analog transmission, which falls under the umbrella field of Fading.
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