Low-Complexity Grassmannian Quantization Based on Binary Chirps
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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2022 IEEE Wireless Communications and Networking Conference, WCNC 2022, pp. 1105-1110, IEEE Wireless Communications and Networking Conference ; Volume 2022-April
Abstract
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.Description
Funding Information: ACKNOWLEDGMENT This work was funded by the Academy of Finland (grants 319484, 334539). Publisher Copyright: © 2022 IEEE.
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Pllaha, T, Heikkila, E, Calderbank, R & Tirkkonen, O 2022, Low-Complexity Grassmannian Quantization Based on Binary Chirps. in 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022. IEEE Wireless Communications and Networking Conference, vol. 2022-April, IEEE, pp. 1105-1110, IEEE Wireless Communications and Networking Conference, Austin, Texas, United States, 10/04/2022. https://doi.org/10.1109/WCNC51071.2022.9771694