Combining Channel Sounding and Guessing Random Additive Noise Decoding
Name
millward-janem7-sm-eecs-2024-thesis.pdf
Description
Thesis PDF
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6.16 MB
Format
Adobe PDF
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3191f7273b34a98aaa37f4155eb8a9db
Author(s)
Millward, Jane Avril
Advisor(s)
Médard, Muriel
Rangaswamy, Muralidhar
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
This thesis investigates how channel estimation can be used to improve the performance of Guessing Random Additive Noise Decoding. The trade-off between devoting resources to channel sounding and data transmission is investigated for pilot symbol assisted modulation schemes. Using a soft-information variant of the GRAND algorithm called Ordered Reliability Bit Guessing Random Additive Noise Decoding- Approximate Independence (ORBGRAND-AI), it is shown that by accounting for the correlation between received symbols bit and block error rate improvements can be obtained. This thesis also considers the achievable communications rate of ORBGRAND-AI when different estimators are used to provide channel estimates. Finally, this thesis investigates the use of ORBGRAND-AI in channels subjected to inter-symbol interference (ISI).
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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