News & Events
The Advanced Network Colloquium Series

We consider the problem of communicating data from multiple traffic streams over an ad-hoc wireless network with time varying channels, user mobility, and possible transmission errors. Our network model is well suited for stochastic environments where exact channel conditions are difficult to assess, such as underwater networks of acoustic transmitters (where ocean dynamics and large delays create channel uncertainty), and land networks with mobility (where knowledge of which receivers are currently within transmission range may be uncertain). To communicate in these extreme environments, we exploit the broadcast advantage of wireless networks. Specifically, a single transmission might be overheard by a set of potential receivers. This creates a natural multi-receiver diversity gain, where the probability that at least one node successfully receives the transmission can be much larger than the success probability of any pre-specified receiver. To fully exploit the multi-receiver diversity gain, network routing algorithms must be designed with the flexibility of dynamically adjusting routing decisions after each packet is transmitted. This functionality affects network design at all networking layers, and is a hot topic of current research. In this talk, we present novel cross-layer control algorithms for routing, resource allocation, and flow control, with the goal of optimizing performance metrics of throughput, fairness, and power expenditure. A simple distributed flow control and routing algorithm DIVBAR (Diversity Backpressure Routing) is introduced and shown to achieve optimality subject to a given multiple access structure. The challenge of choosing a good multiple access structure is also discussed. Simulations of basic and enhanced versions of DIVBAR are also provided to illustrate the performance gains over existing algorithms.
Our results are based on the stochastic network optimization techniques developed in our recent work (INFOCOM 2003, 2005, CISS 2006, NOW Foundations & Trends 2006). This work is supported in part by one or both of the following: the National Science Foundation Grant OCE 0520342, the DARPA IT-MANET program.

Michael J. Neely received B.S. degrees in both Electrical Engineering and Mathematics from the University of Maryland, College Park, in 1997. He was then awarded a 3 year Department of Defense NDSEG Fellowship for graduate study at the Massachusetts Institute of Technology, where he received an M.S. degree in 1999 and a Ph.D. in 2003, both in Electrical Engineering.
During the summer of 2002, he worked in the Distributed Sensor Networks group at Draper Labs in Cambridge. In 2004 he joined the faculty of the Electrical Engineering Department at the University of Southern California, where he is currently an Assistant Professor. His research is in the area of stochastic network optimization for satellite and wireless networks, mobile ad-hoc networks, and queueing systems. Michael is a member of Tau Beta Pi and Phi Beta Kappa.