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Title: Low-Complexity Grassmannian Quantization Based on Binary Chirps
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 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.  more » « less
Award ID(s):
1908730
NSF-PAR ID:
10350445
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2022 IEEE Wireless Communications and Networking Conference
Page Range / eLocation ID:
1105 to 1110
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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