Cognitive radio engine parametric optimization utilizing Taguchi analysis

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.

A Survey of Wireless Communication Technologies & Their Performance for High Speed Railways

“…Among them is also the Rail-Cognitive Radio (Rail-CR). Amanna et al are involved with this development [22]. In their publications, they have reasoned that network management in HSR is not trivial due to unpredictable usage, multiple users striving for bandwidth, multiple sources of interference, etc.…”

“…FRA also points out time-to-decision problems. Amanna et al [22] have employed the Taguchi Analysis model to address this problem. According to the authors, CR with CE takes time to learn parameters in experimental test cases.…”