The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

This paper investigates task-oriented communication for edge inference, where a low-end
edge device transmits the extracted feature vector of a local data sample to a powerful edge …

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 …

This paper investigates task-oriented communication for multi-device cooperative edge
inference, where a group of distributed low-end edge devices transmit the extracted features …

We propose deep learning based communication methods for adaptive-bandwidth
transmission of images over wireless channels. We consider the scenario in which images …