Assessment of spectrum sensing using support vector machine combined with principal component analysis | |
| Author | Mahanta, Manash |
| Call Number | AIT Thesis no.TC-21-01 |
| Subject(s) | Cognitive radio networks Sensor networks Machine Learning Kernel functions |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Telecommunications |
| Publisher | Asian Institute of Technology |
| Abstract | Cognitive Radio is an up-and-coming technology to help rectify the issue of under utilization of allocated spectrum as well as to meet the increasing demand for free spectrum. Spectrum sensing is the bedrock behind this novel technological idea. In this paper, we perform spectrum sensing using Support Vector Machine (SVM) which is the most accepted Machine Learning algorithm. We evaluate the performance of var ious Kernel functions used in SVM, as well as how the performance of the learning algorithm changes as we apply Principal Component Analysis (PCA) and vary the Ker nel scale. We then compare the training time of the SVM Kernels. We also calculate the contributions of power, variance, skewness, and kurtosis of the received signal towards the decision making process of the learning algorithm. |
| Year | 2021 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Telecommunications (TC) |
| Chairperson(s) | Attaphongse Taparugssanagorn |
| Examination Committee(s) | Poompat Saengudomlert;Teerapat Sanguankotchakorn |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2021 |