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

Abstract

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.

Wireless Communication: Applications Security and Reliability—Present and Future

“…The presented experimentally measured data and their interpretation are important in the context of RF source localization. In [30], a low-complexity NUC demapping algorithm, i.e., the SCSR algorithm, is proposed. SCSR algorithm creates the subsets based on the quadrant of 2D-NUC received symbol or the sign of the I/Q component after 1D-NUC received symbol is decomposed.…”

Improving Log-Likelihood Ratio Estimation with Bi-Gaussian Approximation under Multiuser Interference Scenarios

“…[ 14 ] proposed an algorithm by taking full advantage of the symmetric characteristics of symbol mapping. For massive-order non-uniform constellations, low-complexity demapping algorithms were proposed in [ 15 , 16 ] for one- and two-dimension constellations, respectively, and [ 17 ] proposed a universal low-complexity demapper for non-uniform constellations. For index modulation, a low-complexity LLR calculation algorithm was proposed in [ 18 ].…”

Introducing 4D Geometric Shell Shaping for Mitigating Nonlinear Interference Noise

“…While simple unconstrained optimizations provide the best theoretical possible performance, they are time consuming, might not lead to a global optimum, and generally provide unstructured results. From a practical point of view, well-structured constellations are always preferred since they allow for more efficient demapping strategies (e.g., by utilizing separability and symmetry of the constellations) [30], [31].…”