Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning

We propose a novel method to optimize the structure of factor graphs for graph-based inference. As an example inference task, we consider symbol detection on linear inter-symbol interference channels. The factor graph framework has the potential to yield low-complexity symbol detectors. However, the sum-product algorithm on cyclic factor graphs is suboptimal and its performance is highly sensitive to the underlying graph. Therefore, we optimize the structure of the underlying factor graphs in an end-to-end manner using machine learning. For that purpose, we transform the structural optimization into a clustering problem of low-degree factor nodes that incorporates the known channel model into the optimization. Furthermore, we study the combination of this approach with neural belief propagation, yielding near-maximum a posteriori symbol detection performance for specific channels.Comment: Submitted to ICASSP 202

End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search

As the demand for higher data throughput in coherent optical communication systems increases, we need to find ways to increase capacity in existing and future optical communication links. To address the demand for higher spectral efficiencies, we apply end-to-end optimization for joint geometric and probabilistic constellation shaping in the presence of Wiener phase noise and carrier phase estimation. Our approach follows state-of-the-art bitwise auto-encoders, which require a differentiable implementation of all operations between transmitter and receiver, including the DSP algorithms. In this work, we show how to modify the ubiquitous blind phase search (BPS) algorithm, a popular carrier phase estimation algorithm, to make it differentiable and include it in the end-to-end constellation shaping. By leveraging joint geometric and probabilistic constellation shaping, we are able to obtain a robust and pilot-free modulation scheme improving the performance of 64-ary communication systems by at least 0.1 bit/symbol compared to square QAM constellations with neural demappers and by 0.05 bit/symbol compared to previously presented approaches applying only geometric constellation shaping

Approximate Maximum a Posteriori Carrier Phase Estimator for Wiener Phase Noise Channels using Belief Propagation

The blind phase search (BPS) algorithm for carrier phase estimation is known to have sub-optimal performance for probabilistically shaped constellations. We present a belief propagation based approximate maximum a posteriori carrier phase estimator and compare its performance with the standard and an improved BPS algorithm.Comment: Accepted for presentation at European Conference on Optical Communications 202

Optimized Geometric Constellation Shaping for Wiener Phase Noise Channels with Viterbi-Viterbi Carrier Phase Estimation

The Viterbi & Viterbi (V&V) algorithm is well understood for QPSK and 16-QAM, but modifications are required for higher-order modulation formats. We present an approach to extend the standard V&V algorithm for higher-order modulation formats by modifying the transmit constellation with geometric constellation shaping.Comment: Accepted for presentation at European Conference on Optical Communications 202

Genome-wide association analysis of pulse wave velocity traits provide new insights into the causal relationship between arterial stiffness and blood pressure.

BACKGROUND:The pathophysiology of arterial stiffness is not completely understood. Pulse wave velocity (PWV) is an established marker for arterial stiffness. We compare genetics of three PWV modes, namely carotid-femoral PWV (cfPWV), brachial-ankle (baPWV) and brachial-femoral (bfPWV), reflecting different vascular segments to analyse association with genetic variants, heritability and genetic correlation with other biological traits. Furthermore we searched for shared genetic architecture concerning PWV, blood pressure (BP) and coronary artery disease (CAD) and examined the causal relationship between PWV and BP. METHODS AND RESULTS:We performed a genome-wide association study (GWAS) for cfPWV, baPWV and bfPWV in LIFE-Adult (N = 3,643-6,734). We analysed the overlap of detected genetic loci with those of BP and CAD and performed genetic correlation analyses. By bidirectional Mendelian Randomization, we assessed the causal relationships between PWV and BP. For cfPWV we identified a new locus with genome-wide significance near SLC4A7 on cytoband 3p24.1 (lead SNP rs939834: p = 2.05x10-8). We replicated a known PWV locus on cytoband 14q32.2 near RP11-61O1.1 (lead SNPs: rs17773233, p = 1.38x10-4; rs1381289, p = 1.91x10-4) For baPWV we estimated a heritability of 28% and significant genetic correlation with hypertension (rg = 0.27, p = 6.65x10-8). We showed a positive causal effect of systolic blood pressure on PWV modes (cfPWV: p = 1.51x10-4; bfPWV: p = 1.45x10-3; baPWV: p = 6.82x10-15). CONCLUSIONS:We identified a new locus for arterial stiffness and successfully replicated an earlier proposed locus. PWV shares common genetic architecture with BP and CAD. BP causally affects PWV. Larger studies are required to further unravel the genetic determinants and effects of PWV