Low-Complexity Grassmannian Quantization Based on Binary Chirps | IEEE Conference Publication | IEEE Xplore

Low-Complexity Grassmannian Quantization Based on Binary Chirps


Abstract:

We consider autocorrelation-based low-complexity decoders for identifying Binary Chirp codewords from noisy signals in N = 2m dimensions. The underlying algebraic structu...Show More

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.
Date of Conference: 10-13 April 2022
Date Added to IEEE Xplore: 16 May 2022
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Conference Location: Austin, TX, USA

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I. Introduction

Subspaces of unit norm complex valued vectors modulo overall phase rotations are of interest in many information processing tasks, ranging from non-coherent wireless communication [1], [2], [3] and activity detection [4] to vector quantization of Channel State Information (CSI) [5], [6], [7], [8] and deep learning [9], while also being the fundamental objects of interest in finite dimensional quantum mechanics [10]. For communication [2], [3], quantization [5], [7], [8] and quantum coding [11] scenarios, codebook based approaches are essential.

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