Topic：Beamforming Methodologies for Cloud Radio Access Networks

Time：2014年1月20日（周一）上午9:30-10:30

Venue：信电大楼-215学术厅

Speaker：Zhang Jun, Visiting Assistant Professor

           Hong Kong University of Science and Technology

Biography

Dr. Jun Zhang is a Visiting Assistant Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). He received the Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin in 2009. He was an intern at AT&T Labs in the summer of 2007 and 2008, working on MIMO techniques in WiMAX and 3GPP-LTE systems. Dr. Zhang is co-author of the book “Fundamentals of LTE” (Prentice-Hall, 2010). His research interests are in wireless communications and networking, and green communications. He has served on TPCs of different international conferences including IEEE ICC, VTC, Globecom, WCNC, PIMRC, etc.  He served as a MAC track co-chair for IEEE WCNC 2011.

Abstract

The Cloud Radio Access Network (Cloud-RAN) is a transformative network architecture for future cellular networks. It provides a cost-effective way to improve both the network capacity and energy efficiency by shifting the baseband signal processing to a single baseband unit (BBU) pool, which enables centralized signal processing. The new architecture of Cloud-RAN brings unique advantages as well as new design challenges. In particular, new computationally efficient beamforming methodologies will be needed to improve performance of such large-scale cooperative networks. In this talk, two different beamforming problems will be discussed. First, a group sparse beamforming method will be introduced to achieve green Cloud-RAN, which will jointly determine coordinated beamforming to reduce transmit power and adaptively switch off access points to save transport network power consumption; next, a stochastic coordinated beamforming framework will be proposed to deal with mixed channel state information (CSI) available at the BBU pool, i.e., the BBU pool only has instantaneous values of a subset of all the channel links, which is a practical assumption in large-scale networks. New optimization tools will be introduced to solve these two beamforming problems, i.e., optimization with sparsity-inducing penalties, and joint chance constrained programming.