Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/544670
Title: Studies of optimum resource allocation mechanisms in NOMA MIMO NOMA networks using different technologies and algorithms for improving system throughput and energy efficiency
Researcher: Ravi, Mancharla
Guide(s): Bulo, Yaka
Keywords: 5G Communications
Deep Neural Network
Water Filling Algorithm
University: National Institute of Technology Arunachal Pradesh
Completed Date: 2023
Abstract: The objective of this work is to achieve maximum EE and broad coverage by applying the max-min power control algorithm through sub-channel optimization, resource allocation (RA), access point selection (APS), and user association. The resource allocation (RA) for energy efficiency is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling convert in convex and linear. This work formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam -forming and AP to user connection via MIMO-NOMA. Users are grouped and served by the APs in the backhaul communication, and all APs are organized into clusters that are all served by the macro base station. Further, we used the AP selection and perfect user selection algorithms, as well as MMPCA for the best resource distribution in MIMO-NOMA. As a result, MIMO-NOMA backhaul communication system model has more coverage and improved energy efficiency. However, network complexity is higher, along with increased power consumption due to clustering, user selection, and access point election. Hence, cell-free RIS is used to reduce system complexity and power consumption. newline
Pagination: xxi, 152
URI: http://hdl.handle.net/10603/544670
Appears in Departments:Department of Electronics and Communication Engineering

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01_title.pdfAttached File497.46 kBAdobe PDFView/Open
02_prelim pages.pdf1.25 MBAdobe PDFView/Open
03_contant.pdf239.37 kBAdobe PDFView/Open
04_abstract.pdf355.44 kBAdobe PDFView/Open
05_chapter 1.pdf530.95 kBAdobe PDFView/Open
06_chapter 2.pdf736.36 kBAdobe PDFView/Open
07_chapter 3.pdf1.25 MBAdobe PDFView/Open
08_chapter 4.pdf1.3 MBAdobe PDFView/Open
09_chapter 5.pdf1.63 MBAdobe PDFView/Open
10_chapter 6.pdf1.31 MBAdobe PDFView/Open
11_chapter 7.pdf1.04 MBAdobe PDFView/Open
12_annexures.pdf828.66 kBAdobe PDFView/Open
80_recommendation.pdf230.67 kBAdobe PDFView/Open
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