In many emerging wireless applications, data-driven-based intelligent learning techniques
like machine learning (ML) are gaining popularity to supplement classical model-driven …

There is a growing interest in the wireless communications community to complement the
traditional model-driven design approaches with data-driven machine learning (ML)-based …

A remarkable interest in machine learning-based schemes as key enablers for
nextgeneration intelligent wireless systems has been observed. Most of the existing learning …

Machine learning and data-driven approaches have recently received considerable
attention as key enablers for next-generation intelligent networks. Currently, most existing …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather
immense data via onboard sensors and mobile applications. This offers an amazing …

To harness the benefits of machine learning (ML), users often face the challenge of sharing
their private data with a central entity for model training. However, data sharing can be …

The advances of both computation and communication technologies facilitate the
exploitation of massive data generated by mobile devices. It is attractive to leverage these …

To facilitate the deployment of machine learning in resource and privacy-constrained
systems such as the Internet of Things, federated learning (FL) has been proposed as a …

In order to meet the extremely heterogeneous requirements of the next generation wireless
communication networks, research community is increasingly dependent on using machine …