Cognitive Radio Essay -- Technology

A problem facing cognitive radio is the need for accurate estimations of performance metrics. Performance estimation algorithms are limited when facing new situations. For example, heuristics, such as genetic algorithms (GA), require specific knowledge about the interference conditions in order to adapt fitness functions. This paper presents an experimental design approach that analyzes performance results of a small set of configurations to create an empirical model. The method overcomes the need for specific knowledge of the channel or noise environment and is capable of addressing new situations.par This problem is difficult given the limitations inherent in any theoretical system model and the complexity of sensing the wireless environment. Existing GA-based cognitive engines identify radio configuration settings based on mathematical models for defining objective functions cite{Rondeau2007}. The methods rely on additional knowledge of noise conditions in order to customize the objective functions to the current environment. Advancements in the decision-making architecture tied case-based reasoning to the GA in order to increase time-to-decision and take advantage of past experiences cite{He2009}. However, CBR relies on the assumption that a past decision will work in the current situation if the two are similar enough. In both methods, the identified solution's true performance is unknown until after it is implemented on the system.par Theoretical models of wireless performance rely on assumptions in the channel conditions, and often do not represent the actual situation. In contrast, statistical methods base all conclusions from empirical evidence without requiring knowledge of the channel or interference conditions. T... ...rameters settings are then pared down by repeating the DOE with focus on another response meter. This process is repeated for each response meter available until a final parameter setting is identified. The authors developed a reconfiguration algorithm that draws its decision from multivariate DOE analysis on the system. The algorithm was implemented on an open-source software controller for off-the-shelf 802.11 wireless cards cite{Weingart2007}.par In contrast, we implement RSM experimental design that leads to quadratic models as opposed to linear. This approach increases accuracy and identifies overall better solutions. We implement the techniques on a software defined radio platform more indicative of deployable cognitive radio. Our focus emphasis the statistical fit performance of different designs and contrasts performance to a reference heuristic engine.