Third Generation Wireless Systems

Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links.

In April 1995, WINLAB (the Wireless Infonnation Network Lab­ oratory at Rutgers University) hosted the Fifth WINLAB Workshop on Third Generation Wireless Infonnation Networks. This workshop brings together a select group of experts interested in the future of Personal Communications, Mobile Computing and other services supported by wireless communications. As a sequel to Kluwer books on previous WINLAB workshops,l this volume assembles written versions of presentations of the Fifth Workshop. The last few years have been exciting for the field of wireless communications. The second generation systems that have absorbed our attention during those years are becoming commercial realities. Everyone is looking forward to PCS, especially in light of the recent auctions. We see an explosion of technical alternatives for meeting the demand for wireless communications. We also have applications in search of the best technologies rather than the reverse. The papers included provide new insights into many of the issues needing resolution for the successful introduction of the new services by the end of the decade. The authors represent views from both industry and universities from a number of nations. They are grouped into four main categories: Architecture, Radio Resource Management, Access, and Mobile Data, Mobile Networks.

Bridging the gap between the video compression and communication communities, this unique volume provides an all-encompassing treatment of wireless video communications, compression, channel coding, and wireless transmission as a joint subject.