Covariance-Aware Demapping on Fourier-Curve Constellations
Bin Han, Muxia Sun, H. Vincent Poor, and Hans D. Schotten

TL;DR
This paper introduces a covariance-aware demapping technique for Fourier-curve constellations that improves error performance by approximately 5 dB over traditional methods, with manageable computational and storage costs.
Contribution
It develops a practical baseband demapper implementing a rank-one covariance correction for Fourier-curve constellations, enhancing decoding performance in coded communication links.
Findings
Extends BLER10^{-1} range by ~5 dB over Euclidean-mismatched decoder.
Achieves this gain with 50-100% more multiply-accumulate operations per symbol.
LUT quantization to 6 bits does not degrade performance at tested points.
Abstract
Injecting artificial noise (AN) along the tangent space of a curved constellation makes each transmitted symbol induce a Gaussian observation with a symbol-dependent rank-one covariance, so the matched maximum-likelihood (ML) decoder differs from the Euclidean nearest-neighbor decoder by a single rank-one correction per candidate. We develop a baseband-demapper realization of this correction for the Fourier-curve constellation and instantiate a regular low-density parity-check (LDPC)-coded link at . Against four baselines (Euclidean-mismatched, flat-constellation isotropic-AN, no-AN, and same-spectral-efficiency narrowband), the matched decoder extends the BLER operating range by approximately \,dB over the Euclidean-mismatched counterpart on the same tangent-AN transmitter, at a cost of additional multiply-accumulate operations per symbol…
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