Low-Complexity Grassmannian Quantization Based on Binary Chirps

Tefjol Pllaha, Elias Heikkila, Robert Calderbank, Olav Tirkkonen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)
79 Downloads (Pure)


We consider autocorrelation-based low-complexity decoders for identifying Binary Chirp codewords from noisy signals in N = 2m dimensions. The underlying algebraic structure enables dimensionality reduction from N complex to m binary di- mensions, which can be used to reduce decoding complexity, when decoding is successively performed in the m binary dimensions. Existing low-complexity decoders suffer from poor performance in scenarios with strong noise. This is problematic especially in a vector quantization scenario, where quantization noise power cannot be controlled in the system. We construct two improvements to existing algorithms; a geometrically inspired algorithm based on successive projections, and an algorithm based on adaptive decoding order selection. When combined with a breadth-first list decoder, these algorithms make it possible to approach the performance of exhaustive search with low complexity.

Original languageEnglish
Title of host publication2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Number of pages6
ISBN (Electronic)978-1-6654-4266-4
Publication statusPublished - 16 May 2022
MoE publication typeA4 Conference publication
EventIEEE Wireless Communications and Networking Conference - Austin, United States
Duration: 10 Apr 202213 Apr 2022

Publication series

NameIEEE Wireless Communications and Networking Conference
ISSN (Print)1525-3511


ConferenceIEEE Wireless Communications and Networking Conference
Abbreviated titleWCNC
Country/TerritoryUnited States


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