Combining Channel Sounding and Guessing Random Additive Noise Decoding

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).

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Millward, Jane Avril

Advisors dc:contributor.advisor

• Médard, Muriel
• Rangaswamy, Muralidhar

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156274

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156274