Low-Complexity Near-Optimum Symbol Detection Based on Neural Enhancement of Factor Graphs

We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.Comment: revised version. arXiv admin note: text overlap with arXiv:2203.0333

Consensus Strings with Small Maximum Distance and Small Distance Sum

The parameterised complexity of consensus string problems (Closest String, Closest Substring, Closest String with Outliers) is investigated in a more general setting, i. e., with a bound on the maximum Hamming distance and a bound on the sum of Hamming distances between solution and input strings. We completely settle the parameterised complexity of these generalised variants of Closest String and Closest Substring, and partly for Closest String with Outliers; in addition, we answer some open questions from the literature regarding the classical problem variants with only one distance bound. Finally, we investigate the question of polynomial kernels and respective lower bounds

Local Message Passing on Frustrated Systems

Message passing on factor graphs is a powerful framework for probabilistic inference, which finds important applications in various scientific domains. The most wide-spread message passing scheme is the sum-product algorithm (SPA) which gives exact results on trees but often fails on graphs with many small cycles. We search for an alternative message passing algorithm that works particularly well on such cyclic graphs. Therefore, we challenge the extrinsic principle of the SPA, which loses its objective on graphs with cycles. We further replace the local SPA message update rule at the factor nodes of the underlying graph with a generic mapping, which is optimized in a data-driven fashion. These modifications lead to a considerable improvement in performance while preserving the simplicity of the SPA. We evaluate our method for two classes of cyclic graphs: the 2x2 fully connected Ising grid and factor graphs for symbol detection on linear communication channels with inter-symbol interference. To enable the method for large graphs as they occur in practical applications, we develop a novel loss function that is inspired by the Bethe approximation from statistical physics and allows for training in an unsupervised fashion.Comment: To appear at UAI 202

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

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

A general approach for robust integrated polarization rotators

Integrated polarization rotators suffer from very high sensitivity to fabrication errors. A polarization rotator scheme that substantially increases fabrication tolerances is proposed. In the proposed scheme, two tunable polarization phase shifters are used to connect three rotator waveguide sections. By means of properly setting the polarization phase shifters, fabrication errors are compensated and perfect polarization rotation is achieved. Analytical conditions are shown that determine the maximum deviation that can be corrected with the proposed scheme. A design example is discussed, where the thermo-optic effect is used to provide the required tunable polarization phase shifting. Calculated 40dB extinction ratio is shown in presence of fabrication errors that would yield a 4dB extinction ratio in the conventional approach. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.The authors want to aknowledge Universidad de Málaga alaga, Campus de Excelencia Internacional Andalucia Tech for their support

The PANOPTIC Camera: A Plenoptic Sensor with Real-Time Omnidirectional Capability

A new biologically-inspired vision sensor made of one hundred "eyes” is presented, which is suitable for real-time acquisition and processing of 3-D image sequences. This device, named the Panoptic camera, consists of a layered arrangement of approximately 100 classical CMOS imagers, distributed over a hemisphere of 13cm in diameter. The Panoptic camera is a polydioptric system where all imagers have their own vision of the world, each with a distinct focal point, which is a specific feature of the Panoptic system. This enables 3-D information recording such as omnidirectional stereoscopy or depth estimation, applying specific signal processing. The algorithms dictating the image reconstruction of an omnidirectional observer located at any point inside the hemisphere are presented. A hardware architecture which has the capability of handling these algorithms, and the flexibility to support additional image processing in real time, has been developed as a two-layer system based on FPGAs. The detail of the hardware architecture, its internal blocks, the mapping of the algorithms onto the latter elements, and the device calibration procedure are presented, along with imaging result

Differential immunomodulatory effects of head and neck cancer-derived exosomes on B cells in the presence of ATP

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy. Tumor-derived exosomes (TEX) have immunoregulatory properties. Adenosine triphosphate (ATP) and its immunosuppressive precursor adenosine (ADO) have been found in cancerous tissue. We investigated the effect of TEX on B cells in the presence of ATP. TEX were isolated from human HNSCC cell line (PCI-13) cultures and co-cultured with peripheral blood B cells of healthy donors, with or without TEX in different concentrations and with or without a low (20 µM) or high (2000 µM) ATP dose. We were able to demonstrate that TEX inhibit B-cell proliferation. The addition of TEX to either ATP concentration showed a decreasing trend in CD39 expression on B cells in a dose-dependent manner. High ATP levels (2000 µM) increased apoptosis and necrosis, and analysis of apoptosis-associated proteins revealed dose-dependent effects of ATP, which were modified by TEX. Altogether, TEX exhibited dual immunomodulatory effects on B cells. TEX were immunosuppressive by inhibiting B-cell proliferation; they were immunostimulatory by downregulating CD39 expression. Furthermore, TEX were able to modulate the expression of pro- and anti-apoptotic proteins. In conclusion, our data indicate that TEX play an important, but complex, role in the tumor microenvironment