Robust Design of Wireless Backhaul Networks Using Convex Polyhedral Representations of Uncertainty

Our earlier work [1] outlined key ideas in a theory of robust traffic distribution independent design of networks. Robustness is achieved by replacing the traffic matrix by a set of linear constraints representing gross traffic properties, on the traffic elements. The new formulation is more powerful than the pre-existing formulation, and, using linear programming, we can obtain traffic distribution-independent bounds on network cost under a variety of assumptions on traffic characteristics. We can derive maximal/minimal bounds for wireless ATM backhauls, which are exact without statistical multiplexing, and function as an adequate engineering approximation assuming statistical multiplexing. Our work is the first to be able to derive guaranteed upper and lower bounds on various parameters in wireless backhaul networks.