{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156274"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156274","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Combining Channel Sounding and Guessing Random Additive Noise Decoding","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_html":"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. 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