AI & ML
Z-Opt: A Near-Optimal Reduced-Complexity Two-Dimensional Grassmannian Constellation
arXiv math.IT0 views
arXiv:2605.04545v2 Announce Type: replace Abstract: Grassmannian constellations achieve the capacity of noncoherent communications over Rayleigh fading channels at high SNR, yet their efficient construction remains challenging. In this paper, we present two construction methods for Grassmannian constellations on G(2,1), termed S-Opt and Z-Opt, and propose two low-complexity detectors. Both construction and detection are performed on the unit sphere, known as the Bloch sphere in quantum computing. Exploiting the proportionality between the chordal distance on the Grassmann manifold and the Euclidean distance on the Bloch sphere, we derive a theoretical upper bound on the minimum chordal distance from the Fejes-Toth bound. The S-Opt constellation is constructed from sphere-packing solutions and achieves the theoretical limit corresponding to the considered optimal Bloch-sphere packings. The S-Opt detector applies to arbitrary constellations on G(2,1) and requires a number of complex multiplications scaling linearly with the number of receive antennas and logarithmically with the constellation size, and a number of numerical comparisons scaling logarithmically with the constellation size, while achieving the same performance as the GLRT detector. Based on the insight from the S-Opt construction, we further propose the Z-Opt constellation, constructed by stacking regular polygons on the Bloch sphere, whose minimum chordal distance approaches the derived upper bound over the evaluated constellation sizes. The Z-Opt detector requires a number of complex multiplications scaling linearly with the number of receive antennas and a number of numerical comparisons scaling logarithmically with the square root of the constellation size, while achieving the same performance as the GLRT detector for the Z-Opt constellation.
Source:https://arxiv.org/abs/2605.04545PreviousNovel Inconsistency Results for Partial Information DecompositionNextRecovery Models for Linear Batch Codes
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