Low-Complexity Demapping Algorithm for Two-Dimensional Non-Uniform Constellations

Fuentes, Manuel; Vargas, David; Gómez-Barquero, David

doi:10.1109/tbc.2015.2492477

Cited by 33 publications

(32 citation statements)

References 18 publications

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“…In [13], high-order NUCs with constellation sizes of up to 4k-QAM are investigated. A sub-optimal demapper that reduces the total number of distances to be computed when using 2D-NUCs has also been proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Performance of Non-Uniform Rotated Constellations With Multi-RF Transmission Techniques

Fuentes

Giménez

Gómez-Barquero

2016

IEEE Trans. on Broadcast.

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Section: Introductionmentioning

confidence: 99%

Optimization and Performance of Non-Uniform Rotated Constellations With Multi-RF Transmission Techniques

Fuentes

Giménez

Gómez-Barquero

2016

IEEE Trans. on Broadcast.

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“…low order constellations and high code rates, as reference [104] shows. However, RCs may become especially effective when using multi-RF techniques, such as Channel Bonding (CB) [62], providing very high performance gains.…”

Section: Non-uniform Rotated Constellationsmentioning

confidence: 94%

“…With NUCs, optimum demappers calculate the distances from the received signal to all constellation symbols in a different manner, since the shape of the constellation changes. In that sense, Section 4.1 analyzes the demapping complexity, while in Section 4.2 we propose a new low-complexity demapping algorithm that reduces the amount of distances to calculate the LLRs [104]. As an example of application, we provide the results for NUCs optimized in Chapter 3.…”

Section: Low-complexity Demapping and Quantization Algorithmsmentioning

confidence: 99%

“…For instance, using a non-condensed 64NUC with b = 4 and k = 40, the stored LUT requires 6.40 Kbits. In case of condensation, the number of regions can be further reduced, as proposed in [104]. For example, using a 64NUC optimized for a CR 2/15, the number of regions is reduced to M = 4, so the number of bits required is only b = 2.…”

Section: Voronoi Regions Selection Algorithmmentioning

confidence: 99%

“…In the first step, the constellation symbols are grouped in clusters and removed, since they hinder the calculation of the Voronoi Regions. This step introduces an insignificant loss in performance, since lineal Euclidean distances among condensed symbols in normalized NUCs are almost negligible, lower than 10 −2 [104]. In order to detect clusters, an array L with polar coordinates (radius and angle) is defined, for each constellation symbol.…”

Section: Voronoi Regions Selection Algorithmmentioning

confidence: 99%

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Non-Uniform Constellations for Next-Generation Digital Terrestrial Broadcast Systems

Muela¹

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Nowadays, the digital terrestrial television (DTT) market is characterized by the high capacity needed for high definition TV services, and the limited spectrum available. There is a need for an efficient use of the broadcast spectrum, which requires new technologies to guarantee increased capacities. NonUniform Constellations (NUC) arise as one of the most innovative techniques to approach those requirements. These constellations have been implemented in next-generation broadcast systems such as DVB-NGH (Digital Video Broadcasting -Next Generation Handheld) or ATSC 3.0 (Advanced Television Systems Committee -Third Generation). NUCs reduce the gap between uniform Gray-labelled Quadrature Amplitude Modulation (QAM) constellations and the theoretical unconstrained Shannon limit. With these constellations, symbols are optimized in both in-phase (I) and quadrature (Q) components by means of signal geometrical shaping, considering a certain signal-to-noise ratio (SNR) and channel model.There are two types of NUC, depending on the number of real-valued dimensions considered in the optimization process, i.e. one-dimensional and two dimensional NUCs (1D-NUC and 2D-NUC, respectively). 1D-NUCs maintain the squared shape from QAM, but relaxing the distribution between constellation symbols in a single component, with non-uniform distance between them. These constellations provide better SNR performance than QAM, without any demapping complexity increase. 2D-NUCs also relax the square shape constraint, allowing to optimize the symbol positions in both dimensions, thus achieving higher capacity gains and lower SNR requirements. However, the use of 2D-NUCs implies a higher demapping complexity, since a 2D-demapper is needed, i.e. I and Q components cannot be separated.In this dissertation, NUCs are analyzed from both transmit and receive point of views, using either single-input single-output (SISO) or multiple-input multiple-output (MIMO) antenna configurations. In SISO transmissions, 1D-NUCs and 2D-NUCs are optimized for a wide range of SNRs, several channel models and different constellation orders, using the Nelder-Mead optimization 3 ABSTRACT algorithm. The optimization of rotated 2D-NUCs is also investigated, including the rotation angle as an additional variable in the optimization. Even though the demapping complexity is not increased, the SNR gain of these constellations is not significant. The highest rotation gain is obtained for low-order constellations and high SNRs. However, with multi-RF techniques such as Channel Bonding (CB) or Time-Frequency Slicing (TFS), the SNR gain is drastically increased, since I and Q components are transmitted in different RF channels. In this thesis, multi-RF gains of NUCs with and without rotation are provided for some representative scenarios.At the receiver, two different implementation bottlenecks are explored. First, the demapping complexity of all considered constellations is analyzed. Afterwards, two complexity reduction algorithms for 2D-NUCs are proposed. Both algor...

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Filtered Based UFMC Waveform Applied on Joint DVB-T2/NUC System

Honfoga

Dossou

Dassi

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Low-Complexity Demapping Algorithm for Two-Dimensional Non-Uniform Constellations

Cited by 33 publications

References 18 publications

Optimization and Performance of Non-Uniform Rotated Constellations With Multi-RF Transmission Techniques

Optimization and Performance of Non-Uniform Rotated Constellations With Multi-RF Transmission Techniques

Non-Uniform Constellations for Next-Generation Digital Terrestrial Broadcast Systems

Filtered Based UFMC Waveform Applied on Joint DVB-T2/NUC System

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