Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/302908
Title: Enhanced channel design and scrutiny for LTE advanced mimo downlink
Researcher: Sorna Keerthi R
Guide(s): Meena Alias Jeyanthi K
Keywords: LTE-A mimo downlink
Urban micro environment
Zero forcing algorithm
University: Anna University
Completed Date: 2019
Abstract: LTE A downlink transmits the data control signals from the base station enhanced Node B to the mobile user equipment LTE A downlink physical layer processing is essential for information retrieval in user equipment mobile with reduced error rate and high throughput Since the wireless channel is time variant and the channel matrix is always varying by the channel characteristics there is a necessity to analyse the channel parameters and design an efficient channel estimation algorithm to reduce the mean square error compared to the existing optimized intelligent channel estimation methods The channel analysis procedure in the thesis is divided into two parts channel selection and channel estimation For channel selection the different LTE A MIMO channel scenarios are analysed in terms of Block Error Rate BLER Throughput Fraction TPF and Spectral Efficiency SE The experimental simulations prove that at SNR of 15 dB indoor hot is the best channel scenario with lowest block error rate of 10 0 16 high throughput fraction of 31 1 and high spectral efficiency of 9 33 bps Hz The next best channel is urban micro environment with low BLER of 10 0 105 high TPF of 21 4 and more SE of 6 42 bps Hz Since the macro environments like Urban Macro Suburban Macro and Rural Macro are worst in performance with more error A system Also it has been concluded that micro cells are better than macro cells and further micro cells can be replaced by nano cells femto cells etc For implementation and analysis of different interpolation and decoding schemes over LTE A MIMO channel scenarios soft decoding is better than hard decision decoding and MMSE channel estimation interpolation is better than LSE and Zero Forcing algorithm is used as the equalization method newline
Pagination: xxv,220
URI: http://hdl.handle.net/10603/302908
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf.pdf136.21 kBAdobe PDFView/Open
03_abstracts.pdf.pdf65.98 kBAdobe PDFView/Open
04_acknowledgements.pdf.pdf4.95 kBAdobe PDFView/Open
05_contents.pdf.pdf13.74 kBAdobe PDFView/Open
06_list_of_tables.pdf.pdf10.35 kBAdobe PDFView/Open
07_list_of_figures.pdf.pdf71.89 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf.pdf73.41 kBAdobe PDFView/Open
09_chapter1.pdf.pdf248.18 kBAdobe PDFView/Open
10_chapter2.pdf.pdf361.75 kBAdobe PDFView/Open
11_chapter3.pdf.pdf320.67 kBAdobe PDFView/Open
12_chapter4.pdf.pdf712.47 kBAdobe PDFView/Open
13_chapter5.pdf.pdf949.65 kBAdobe PDFView/Open
14_chapter6.pdf.pdf532.56 kBAdobe PDFView/Open
15_conclusion.pdf.pdf101.88 kBAdobe PDFView/Open
16_references.pdf.pdf191.52 kBAdobe PDFView/Open
17_list_of_publications.pdf.pdf84.54 kBAdobe PDFView/Open
80_recommendation.pdf162.25 kBAdobe PDFView/Open
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