Low complexity and high order two-dimensional non-uniform constellations for high capacity broadcasting systems

Barrueco, Jon; Regueiro, Cristina; Montalbán, Jon; Vélez, Manuel; Angueira, Pablo; Kim, Heung-Mook; Park, Sung Ik; Kwon, Soon-Bark

doi:10.1109/bmsb.2016.7521941

Cited by 9 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NUC can be classified into 1D-NUC and 2D-NUC, and its design process has been described in [14][15][16][17][18]. Symmetry is the first characteristic of NUC.…”

Section: Characteristics Of Nucmentioning

confidence: 99%

A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations

Wang

2020

Applied Sciences

View full text Add to dashboard Cite

A non-uniform constellation (NUC) can effectively reduce the gap between bit-interleaved coded modulation (BICM) capacity and Shannon capacity, which has been utilized in recent wireless broadcasting systems. However, the soft demapping algorithm needs a lot of Euclidean distance (ED) calculations and comparisons, which brings great demapping complexity to NUC. A universal low-complexity NUC demapping algorithm is proposed in this paper, which creates subsets based on the quadrant of the two-dimensional NUC (2D-NUC) received symbol or the sign of the in-phase (I)/quadrature (Q) component of the one-dimensional NUC (1D-NUC) received symbol. ED calculations and comparisons are only carried out on the constellation points contained in subsets. To further reduce the number of constellation points contained in subsets, the proposed algorithm takes advantage of the condensation property of NUC and regards a constellation cluster containing several constellation points as a virtual point. Analysis and simulation results show that, compared with the Max-Log-MAP algorithm, the proposed algorithm can greatly reduce the demapping complexity of NUC with negligible performance loss.

show abstract

“…NUC can be classified into 1D-NUC and 2D-NUC, and its design process has been described in [14][15][16][17][18]. Symmetry is the first characteristic of NUC.…”

Section: Characteristics Of Nucmentioning

confidence: 99%

A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations

Wang

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…2D-NUCs designed for low code rates present several constellation symbols situated at the same position in the constellation diagram. This sort of constellations is called condensed 2D-NUCs [34], [35]. Condensation maximizes BICM channel capacity by favoring the capacity of the most significant bits (MSB) while sacrificing the capacity associated to the least significant bits (LSB), which in any case will be degraded by high noise values.…”

Section: Characteristics Of 2d-nucsmentioning

confidence: 99%

“…The condensed demapper takes advantage of the condensation applied to the 2D-NUCs designed for low and mid code rates [17], [34]. When designing a condensed demapper, the most important parameter is the condensation degree (or number of merged points) to be applied to the 2D-NUCs.…”

Section: A Condensed Demapper Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Low Complexity Adaptive Demapper for 2-D Non-Uniform Constellations

Barrueco

Montalbán

Angueira

et al. 2019

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

In this paper, a novel demapper for two-dimensional non-uniform constellations (2D-NUC) is proposed, exploiting the characteristics of these constellations. It represents the combination of two underlying demapping techniques targeting ATSC 3.0 compliant OFDM transceiver. On the one hand, for low code rates, we define a metric to perform condensed demapping. On the other hand, for high code rates, adaptive sub-region demapping is proposed. In this work, a combination of both demapping methods is designed showing comparable performance to the classical Max-log demapper since the gap does not exceed 0.1 dB for all code rates of the ATSC 3.0 standard. Higher complexity reduction, from 79.2% to 95.4%, than state of art 2D-NUC demappers is obtained for 2D-256NUCs. These results are validated for ideal and non-ideal channel state information (CSI) over additive white noise Gaussian (AWGN) and Rayleigh independently and identically distributed (iid) channels. Results are extended to 2D-1kNUCs and 2D-4kNUCs showing demapping complexity reduction from 96% to 99.7% with a negligible impact on performance.

show abstract

“…This demapper was further analyzed in [44]. Following the same strategy, authors in [45] propose high order low-complexity 2D-NUCs with cardinality points 1024 and 4096. Low complexity is achieved via condensation of the closest symbols, and the improvement in the performance compared to 1D comes from the two-dimensional optimization.…”

Section: Non-uniform Constellationsmentioning

confidence: 99%

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

Muela¹

View full text Add to dashboard Cite

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...

show abstract

Low complexity and high order two-dimensional non-uniform constellations for high capacity broadcasting systems

Cited by 9 publications

References 10 publications

A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations

A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations

Low Complexity Adaptive Demapper for 2-D Non-Uniform Constellations

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

Contact Info

Product

Resources

About