Testbed Implementation of a Scalable ARIMA Model for Spectrum Estimation in Cognitive Radio-A Null Hypothesis Approach

Abstract

A two-stage data-driven Spectrum Estimation (SE) technique has been introduced for a Cognitive Radio (CR) system involving Null-Hypothesis approach using robust Chi-Square Goodness of Fit (GoF) for confirmation of the desired signal. Development of optimized scalable ARIMA model concerning data length, lag order and AIC-BIC for frugal SE with minimum response time has been the main contribution. The implementation of the model and subsequent validation have been performed in WARP testbed with an optimized data length of only 250 in ARIMA (3,1,2) model. A response time of 6.25 μ S provides an improvement in peak PSD from existing −40 dB to +45 dB.