Abstract
Cognitive radio (CR) engines often contain multiple system parameters that require careful tuning to obtain favorable overall performance. This aspect is a crucial element in the design cycle yet is often addressed with ad hoc methods. Efficient methodologies are required in order to make the best use of limited manpower, resources, and time. Statistical methods for approaching parameter tuning exist that provide formalized processes to avoid inefficient ad hoc methods. These methods also apply toward overall system performance testing. This article explores the use of the Taguchi method and orthogonal testing arrays as a tool for identifying favorable genetic algorithm (GA) parameter settings utilized within a hybrid case base reasoning/genetic algorithm CR engine realized in simulation. This method utilizes a small number of test cases compared to traditional design of experiments that rely on full factorial combinations of system parameters. Background on the Taguchi method, its drawbacks and limitations, past efforts in GA parameter tuning, and the use of GA within CR are overviewed. Multiple CR metrics are aggregated into a single figure-of-merit for quantification of performance. Desirability functions are utilized as a tool for identifying ideal settings from multiple responses. Kiviat graphs visualize overall CR performance. The Taguchi method analysis yields a predicted best combination of GA parameters from nine test cases. A confirmation experiment utilizing the predicted best settings is compared against the predicted mean, and desirability. Results show that the predicted performance falls within 1.5% of the confirmation experiment based on 9 test cases as opposed to the 81 test cases required for a full factorial design of experiments analysis.
Highlights
Cognitive radios (CRs) incorporate artificial intelligence with wireless communications devices to enable automated decision making and long term learning
This article explores the use of the Taguchi method to identify selection of genetic algorithm (GA) configuration parameters within a CR engine
Desirability functions are a common tool for assessing optimization of multiple responses and are crafted to match the desired HIB or LIB goals [26]
Summary
Cognitive radios (CRs) incorporate artificial intelligence with wireless communications devices to enable automated decision making and long term learning. The specific problem addressed here focuses on implementing strategies that limit the number of required tests needed to identify acceptable parameter values. These same methodologies can be applied to overall system testing. Several systematic frameworks exist that address this problem from a statistical perspective utilizing empirically measured results These methods include design of experiments (DOE), response surface methodology (RSM), and the Taguchi method utilizing orthogonal arrays (OA). Results are presented only in terms of the input configuration parameters This concept is important from the perspective of system development and deployment, where only a few key individuals may possess detailed knowledge of how components are designed, and others will most likely test and configure the system. The concept of ‘survival of the fittest’ states that the best combination of genes and their resulting chromosomes yields the strongest individual which will survive the longest
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