.png){kind=icon}
.png){kind=icon}
Return
PIXEL: A Novel Detection Algorithm for Super Constellations
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2025.3615402.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The demand for higher data rates in next-generation wireless networks has led to the emergence of super constellations that support large symbol sets while remaining robust to phase noise. However, efficient detection in such constellations is challenging due to the absence of low-complexity methods that operate independently of the constellation structure. Existing approaches, such as the Gaussian amplitude phase detector (GAP-D), provide accurate detection under additive Gaussian white noise (AWGN) and Gaussian phase noise (GPN), but induce highly irregular decision regions in the in-phase/quadrature (I/Q) plane, necessitating an exhaustive search over all constellation symbols that leads to prohibitive complexity in super-constellation regimes. In this letter, we propose PIXEL, a general-purpose detection algorithm that achieves constant-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}1$ </tex-math></inline-formula>) complexity by pixelizing the I/Q plane into a uniform grid, with each cell linked to a small, precomputed set of candidate symbols derived from the GAP-D metric. This enables efficient, near-optimal detection without relying on structured geometries or exhaustive search. Numerical results demonstrate that PIXEL delivers high detection accuracy across diverse constellation designs, providing a flexible and practical solution for high-throughput communication systems.
Keywords:
Super constellations
high-order modulations
phase noise
symbol detection
6G networks
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W