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Stefano Buzzi

Researcher at University of Cassino

Publications -  210
Citations -  13398

Stefano Buzzi is an academic researcher from University of Cassino. The author has contributed to research in topics: MIMO & Telecommunications link. The author has an hindex of 32, co-authored 193 publications receiving 10819 citations. Previous affiliations of Stefano Buzzi include Princeton University.

Papers
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Journal ArticleDOI

What Will 5G Be

TL;DR: This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
Journal ArticleDOI

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

TL;DR: This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.
Posted Content

What Will 5G Be

TL;DR: In this paper, the authors identify key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
Journal ArticleDOI

Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM?: An overview of alternative modulation schemes for improved spectral efficiency

TL;DR: This article provides a review of some modulation formats suited for 5G, enriched by a comparative analysis of their performance in a cellular environment, and by a discussion on their interactions with specific 5G ingredients.
Journal ArticleDOI

Cell-Free Massive MIMO: User-Centric Approach

TL;DR: A user-centric virtual cell approach to CF massive MIMO, wherein each user is served only by a limited number of access points, which requires less backhaul overhead than the CF approach, and outperforms the latter in terms of achievable rate-per-user.