Abstract:We consider autocorrelation-based low-complexity decoders for identifying Binary Chirp codewords from noisy signals in N = 2 m dimensions. The underlying algebraic structure enables dimensionality reduction from N complex to m binary dimensions, 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 sc… Show more
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