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Math @ Duke
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Publications [#384498] of Robert Calderbank
Papers Published
- Pllaha, T; Heikkila, E; Calderbank, R; Tirkkonen, O, Low-Complexity Grassmannian Quantization Based on Binary Chirps,
IEEE Wireless Communications and Networking Conference Wcnc, vol. 2022-April
(January, 2022),
pp. 1105-1110 [doi]
(last updated on 2026/01/16)
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.
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