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

Abstract

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.

Low‐complexity demapping algorithm for two‐dimensional non‐uniform constellations in ATSC 3.0

“…However, the improvement comes at a price of BER performance degradation induced by distance approximation. Barrueco et al showed that to achieve a balance between demapping complexity and BER performance, the distance between any two points in a cluster is better limited to less than 0.1 [19]. In this paper, we adopt 0.05 as the distance limit.…”

“…Compared with the Log‐MAP algorithm, the Max‐Log‐MAP algorithm [20, 21], and Fuentes' method, the proposed 2D‐demapping method is shown to have a significantly lower complexity. Moreover, the subsets in the proposed method, unlike those in the adaptive‐subregion demapping algorithm [19], are determined beforehand. Therefore, the proposed method has fixed complexity and is suitable for hardware implementation.…”

“…The concept of the third approach is to consider only constellation points in the neighbourhood of the received signal. Barrueco et al [19] proposed an adaptive-subregion demapping algorithm which defines an adaptive-size square region around the received signal based on noise power and then considers only the constellation points inside the region. The adaptivesubregion demapping algorithm, together with CSR, further reduces the number of points in LLR computation, but at the same time imposes the computational overhead for identifying all constellation points inside the subregion.…”

Adaptive Irregular Constellations for Full-Duplex Relaying With Residual Self-Interference

“…As mentioned above, using a set of non-equally spaced constellations can enhance the performance of a given system in different aspects but it comes with some implementation complexity. Specifically, it was shown in [10], [11] that employing non-uniform constellations introduces a bottleneck in real receiver implementations where demappers are supposed to calculate the distance to all constellation points. In addition to the demapping complexity, the use of multiple constellations requires look-up table (LUT)-based structures where the constellation points are stored in them along with suitable labels.…”