Multi-university five-year project sponsored by DARPA

Andrea Goldsmith (Stanford, PI); Stephen Boyd (Stanford, co- PI); Ramesh Johari (Stanford, co-PI); Todd Coleman (UIUC, co-PI); Michelle Effros (Caltech, co-PI); Ralf Koetter (UIUC, co-PI); Sean Meyn (UF, co-PI); Pierre Moulin (UIUC, co-PI); Muriel Medard (MIT, co-PI); Asuman Ozdaglar (MIT, co-PI); Devavrat Shah (MIT, co-PI); Lizhong Zheng (MIT, co-PI)

**Project Summary **This abstract is from an IEEE Communications Magazine article about the project,

We describe a new theoretical framework for determining fundamental performance limits of wireless ad hoc networks. The framework expands the traditional definition of Shannon capacity to incorporate notions of delay and outage. Novel tools are described for upper and lower bounding the network performance regions associated with these metrics under a broad range of assumptions about channel and network dynamics, state information, and network topologies. We also develop a flexible and dynamic interface between network applications and the network performance regions to obtain the best end-to-end performance. Our proposed framework for determining performance limits of wireless networks embraces an interdisciplinary approach to this challenging problem that incorporates Shannon Theory along with network theory, combinatorics, optimization, stochastic control, and game theory. Preliminary results of this approach are described and promising future directions of research are outlined.

A. Goldsmith, M. Effros, R. Koetter, M. Medard, A. Ozdaglar, and L. Zheng, Beyond Shannon: The quest for fundamental per- formance limits of wireless ad hoc networks, IEEE Communica- tions Magazine, May 2011.

M. Effros, A. Goldsmith and M. Medard, The rise of instant wireless networks, Scientific American, April 2010.

The grand research challenge of developing and exploiting a more powerful Information Theory for MANETs initiated the DARPA Information Theory of Mobile Ad Hoc Networks (ITMANET) program. The programs hypothesis is that a better understanding of these fundamental capacity limits will lead to insights and implications for network design and deployment, including an optimal layering of the protocol stack defining the appropriate interface between applications and their underlying networks.

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