Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/571711
Title: | Cooperative spectrum management in cognitive radio networks using deep learning techniques |
Researcher: | Suriya M |
Guide(s): | Sumithra M G |
Keywords: | Computer Science Computer Science Information Systems Engineering and Technology |
University: | Anna University |
Completed Date: | 2024 |
Abstract: | Spectrum scarcity remains one of the major issues in the era of newlinewireless communication systems, and much of that must be attributed to newlineinefficient spectrum usage amongst licensed users. Governmental regulatory newlineorganizations like the Federal Communications Commission (FCC) manage newlinetraditional Radio Frequency (RF) spectrum allocation and establish newlinetransmission restrictions to ensure minimal spectral interference between newlinewireless devices. Improvements in Information and Communication newlineTechnology (ICT) may cause difficulty in spectrum management as new radio newlinespectrum-dependent devices emerge, placing a huge demand on the allocated newlinewireless spectrum. newlineCognitive Radio (CR) networks are an innovative technology that newlinefocuses on the radical shift in radio and networking technologies that ensemble newlinewith the potential to provide major performance gains in optimizing the newlineefficiency of any spectrum. Cognitive radios play a critical role in identifying newlineand sharing unused spectrum for dynamic, spectrum-demanding applications. newlineAs cognitive radio domains have started to progress significantly, new research newlineis required to address some prevailing technical challenges in dynamic newlinespectrum management methods such as spectrum sensing, monitoring, and newlinedynamic spectrum allocation. newline |
Pagination: | xix,146p. |
URI: | http://hdl.handle.net/10603/571711 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 24.96 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.58 MB | Adobe PDF | View/Open | |
03_content.pdf | 31.35 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 83.62 kB | Adobe PDF | View/Open | |
05_chapter1.pdf | 560.87 kB | Adobe PDF | View/Open | |
06_chapter2.pdf | 261.44 kB | Adobe PDF | View/Open | |
07_chapter3.pdf | 821.03 kB | Adobe PDF | View/Open | |
08_chapter4.pdf | 2.55 MB | Adobe PDF | View/Open | |
09_chapter5.pdf | 325.79 kB | Adobe PDF | View/Open | |
10_chapter6.pdf | 28.73 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 137.61 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 68.59 kB | Adobe PDF | View/Open |
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