Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

Damrath, Martin, Hoeher, Peter and Forkel, Gilbert J.M. (2017) Symbol detection based on Voronoi surfaces with emphasis on superposition modulation Digital Communications and Networks, 3 (3). pp. 141-149. DOI 10.1016/j.dcan.2017.01.001.

Full text not available from this repository.

Supplementary data:


A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability (APP) detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two-dimensional digital modulation schemes which cannot be split-up into real and imaginary parts (like phase shift keying and phase-shifted superposition modulation (PSM)) is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.

Document Type: Article
Keywords: Digital modulation Demodulation Detection algorithms Linear approximation
Research affiliation: Kiel University > Kiel Marine Science
Kiel University
OceanRep > The Future Ocean - Cluster of Excellence
Refereed: Yes
DOI etc.: 10.1016/j.dcan.2017.01.001
ISSN: 23528648
Date Deposited: 18 Dec 2017 14:10
Last Modified: 18 Dec 2017 14:10

Actions (login required)

View Item View Item