Exploring Simulated Annealing and Graphical Models for Optimization in Cognitive Wireless Networks

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Executive Summary

In this paper, the authors discuss the design of optimization algorithms for Cognitive Wireless Networks (CWNs). Maximizing the perceived network performance towards applications by selecting appropriate protocols and carrying out cross-layer optimization on the resulting stack is a key functionality of any CWN. They take a "Black box" approach to the problem and study the use of simulated annealing for solving it. To improve the convergence rate of the basic algorithm they apply machine learning techniques to construct graphical models on the perceived relations between network stack parameters and application-specific network utilities. They test the optimizer design both in a simulation environment as well as on a network testbed with low-power radios.

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