2026-09-19T05:23:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1562742024-08-22T04:00:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Médard, Muriel
Rangaswamy, Muralidhar
Millward, Jane Avril
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-08-21T18:53:08Z
2024-08-21T18:53:08Z
2024-05
2024-07-10T12:59:46.274Z
https://hdl.handle.net/1721.1/156274
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).
S.M.
Massachusetts Institute of Technology
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Combining Channel Sounding and Guessing Random Additive Noise Decoding
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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).</Abstract>
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