101 citations · 167 across the 8 of their papers we have counts for
11 papers
Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels
Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
Collaborative inference systems are one of the emerging solutions for deploying deep neural networks (DNNs) at the wireless network edge. Their main idea is to divide a DNN into tw…
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising…
DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques. Very promising results in end-to-end image qual…
AirNet: Neural Network Transmission over the Air
Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location- and time-sensitive, and must be delivered over a…
LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training
Matteo Zecchin, Mahdi Boloursaz Mashhadi, Mikolaj Jankowski +3
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high use…
Federated mmWave Beam Selection Utilizing LIDAR Data
Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung +2
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infra…
Deep Joint Transmission-Recognition for Multi-View Cameras
Ezgi Ozyilkan, Mikolaj Jankowski
We propose joint transmission-recognition schemes for efficient inference at the wireless edge. Motivated by the surveillance applications with wireless cameras, we consider the pe…
Communicate to Learn at the Edge
Deniz Gunduz, David Burth Kurka, Mikolaj Jankowski +3
Bringing the success of modern machine learning (ML) techniques to mobile devices can enable many new services and businesses, but also poses significant technical and research cha…
Wireless Image Retrieval at the Edge
Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
We study the image retrieval problem at the wireless edge, where an edge device captures an image, which is then used to retrieve similar images from an edge server. These can be i…
Joint Device-Edge Inference over Wireless Links with Pruning
Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge. Our goal is to enable efficient and reliable inference at the e…
Deep Joint Source-Channel Coding for Wireless Image Retrieval
Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk
Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compres…