Modeling and Optimizing Heterogeneous Cellular Network Capacity

Lecture / Panel
For NYU Community

Speaker: Professor Jeffrey Andrews

Host Faculty: Professor Ted Rappaport


Given the ever increasing heterogeneity, density, and irregularity of cellular networks, new models are needed for understanding the statistics of SINR, and rate. Unlike in conventional cellular networks, SINR does not map directly to rate, because the rate is largely a function of the congestion on the base station. We begin by reviewing our proposed spatial model and SINR results, before moving onto attempting to understand the optimal rate distribution in HetNets. To do so, the rules used for associating mobile devices with base stations need to be revisited from first principles. We explore this challenging optimization problem from two angles; the first being a fairly traditional optimization approach with several relaxations. Interestingly, we show numerically that the simple "cell range expansion" approach advocated by Qualcomm and adopted by 3GPP is near-optimal despite its simplicity. The second approach is an approximate statistical approach based on stochastic geometry. In both cases, we observe interesting and largely compatible trends, indicating the great benefits available from intelligent association.

About the Speaker

Jeffrey Andrews is a Professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin. His expertise is on both theoretical and practical aspects of wireless systems and networks, recently focused on topics in heterogeneous cellular networks such as femtocells. He has held industry positions at Qualcomm (1995-97) and Intel (1994), as well as consulting for Verizon, the WiMAX Forum, Apple, Intel, Microsoft, Clearwire, Sprint, and NASA.

Dr. Andrews is the co-author of two books, the popular Fundamentals of WiMAX (Prentice-Hall, 2007) and Fundamentals of LTE (Prentice-Hall, 2010), as well as over 250 publications, many highly cited, and several patents. He is an IEEE Fellow, has received five IEEE Best Paper awards, the National Science Foundation CAREER award, and holds a Ph.D. in Electrical Engineering from Stanford University.