International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 2620 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C4928098319/2019©BEIESP DOI:10.35940/ijrte.C4928.098319 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 P d 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 H 0 distribution and set the same threshold at all times. Index Terms: Bootstrap, Cognitive Radio, Multi-Antenna. I. INTRODUCTION The demand for wireless traffic is increasing, especially with internet of things (IoT) trends. According to previous research utilization is between 25% and 85%. Therefore it can increase wireless traffic using maximize spectrum utilization, one of a solution to increasing wireless traffic is efficient of the spectrum, the technology can make efficient is Cognitive Radio. Cognitive Radio has 4 spectra such as spectrum sensing, spectrum management, spectrum mobility, spectrum sharing [1], [2]. Spectrum sensing used to find an unoccupied channel and occupied channel in which if unoccupied channel it means channel include noise or there is no primary user (PU) and if occupied channel it means channel include noise and signal or Revised Manuscript Received on September 15, 2019 Mochammad Haldi Widianto, Informatics, School of Creative Technology Binus Bandung. Rudy Aryanto, Creativepreneurship, School of Creative Technology Binus Bandung. Citra Fadillah, Visual Communication Design, School of Creative Technology Binus Bandung. there is PU [3], Secondary User (SU) can fill the channel. But, the problem is many SU needs an unoccupied channel or assumed multiantenna receiver [4] In multi-antenna detection according to previous research usually using GLRT Approach [4]-[7]. According to previous research there are kind of type detector (generally GLRT Approach), Especially for detector type-3 that can not derive distribution H 0 and H 1 . According to previous research signal processing can combine with bootstrap. Without, know about distribution H 0 and help for detector type-3 to get assumption Probability Detection (P d ). Therefore, using bootstrap for several detectors in this paper [7]-[8]. Bootstrap use resampling to develop the algorithms for spectrum sensing, it includes resampling in fixed sample size testing. In this research, bootstrap-based test sampling is used with a small sample size when the designed asymptotic test statistics fail. and bootstrap resampling is the right choice to reduce the problem of statistical distribution that is difficult to process. These bootstrap properties are explored to achieve the objective of developing spectrum sensing algorithms with a short sensing time [7]. the use of bootstrapping for applications of signal processing methods can be found in [9] and other references. This method has also been successfully applied in some research [9]-[10]. Literature about bootstrap implementation in rare spectrum sensing. In previously research, parametric bootstrapping is used to produce a likelihood ratio test distribution under the null hypothesis. Some research suggest using the bootstrap approach in multi-antenna spectrum sensing that is based on eigenvalue distributions, which have seldom been analyzed in previous adoption studies[11]. There are two kinds of algorithms including the type-3 that will be analyzed, Such as test statistic arithmetics and geometric (TAGM) and TSTBCGLRT. Assuming space-time block code (STBC) as PU and Geometrically-Based Single Bounce (GBSB) as multi-antenna channel [12]-[14]. II. DETECTION MODEL The multi-antenna concept is used because of the many user access from various devices. So much of the research that assumes a decline in methods with multi-antennas is shown in Figure 1: Multi-Antenna Spectrum Sensing using Bootstrap on Cognitive Radio for Internet of Things Application Mochammad Haldi Widianto, Rudy Aryanto, Citra Fadillah