Application of the Rasch model to measuring the performance of cognitive radios

Cognitive radios (CRs) are recent technological developments that rely on artificial intelligence to adapt a radio's performance to suit environmental demands, such as sharing radio frequencies with other radios. Measuring the performance of the cognitive engines (CEs) that underlie a CR's performance is a challenge for those developing CR technology. This simulation study illustrates how the Rasch model can be applied to the evaluation of CRs. We simulated the responses of 50 CEs to 35 performance tasks and applied the Random Coefficients Multidimensional Multinomial Logit Model (MRCMLM) to those data. Our results indicate that CEs based on different algorithms may exhibit differential performance across manipulated performance task parameters. We found that a multidimensional mixture model may provide the best fit to the simulated data and that the two algorithms simulated may respond to tasks that emphasize achieving high levels of data throughput coupled with lower emphasis on power conservation differently than they do to other combinations of performance task characteristics.