Multi-antenna spectrum sensing using bootstrap on cognitive radio for internet of things application

Abstract

Cognitive Radio (CR) is a technology used for other developing technologies like Internet of Things (IoT), one part of CR is spectrum sensing which is useful as an empty spectrum searcher. The use of spectrum is now considered very minimal and raises the problem of scarcity of spectrum. But after testing the real problem is the spectrum in utilization. This problem can be overcome by using efficient utilization of CR technology using Spectrum Sensing. Sensing algorithms that are usually used such as: a suitable filter, energy detector and cyclostationary are not enough because there are many antennas to be detected. In the case of multi-antenna detection, research usually uses the Generalized likelihood ratio test (GLRT) approach. The GLRT Approach Detector also has three types of detectors, type-3 detectors do not determine statistical tests. However, if you use monte carlo or the literacy algorithm, you need a lot of data to get the detector performance. this research will combine algorithms using bootstrap to determine detector performance using small data because using Bootstrap basically only requires a small resampling. The research wants to show if a type-3 detector can help the detector produce good probabilities using little data. The expected result is that the GLRT approach can be combined with a bootstrap for type-3 detectors such as: arithmetic and geometric statistical tests (TAGM) and GLRT time code space code statistical tests (TSTBCGLRT) to help determine assumptions Pd assumptions. Then an experiment was carried out to determine the threshold, by comparing bootstrap with monte carlo, research is expected to show that bootstrap works without a known H0 distribution and set the same threshold at all times.