Estimation of Signal Parameters using Deep Convolutional Neural Networks

This paper introduces a Deep Learning approach for signal parameter estimation in the context of wireless channel modeling. Our work is capable of multidimensional parameter estimation from a signal containing an unknown number of paths. The signal parameters are estimated relative to a predefined grid, providing quasi grid-free, hence, more accurate estimates than previous grid-limited approaches. It requires no prior knowledge of the number of paths, giving it an advantage in terms of complexity compared to existing solutions. Along with the description, we provide an initial performance analysis and a comparison with State-of-the-Art techniques and discuss future research directions

TYK2 Kinase Activity Is Required for Functional Type I Interferon Responses In Vivo

Tyrosine kinase 2 (TYK2) is a member of the Janus kinase (JAK) family and is involved in cytokine signalling. In vitro analyses suggest that TYK2 also has kinase-independent, i.e., non-canonical, functions. We have generated gene-targeted mice harbouring a mutation in the ATP-binding pocket of the kinase domain. The Tyk2 kinase-inactive (Tyk2K923E) mice are viable and show no gross abnormalities. We show that kinase-active TYK2 is required for full-fledged type I interferon- (IFN) induced activation of the transcription factors STAT1-4 and for the in vivo antiviral defence against viruses primarily controlled through type I IFN actions. In addition, TYK2 kinase activity was found to be required for the protein’s stability. An inhibitory function was only observed upon over-expression of TYK2K923E in vitro. Tyk2K923E mice represent the first model for studying the kinase-independent function of a JAK in vivo and for assessing the consequences of side effects of JAK inhibitors

High Resolution Parameter Estimation for Wideband Radio Channel Sounding

&lt;p&gt;Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight and Doppler shift are estimated from observations in frequency, time and space. A maximum likelihood estimation framework allows joint high-resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements.&lt;br&gt; &lt;br&gt; The used model should be as simple as possible since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept orthogonal. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase.&lt;br&gt; &lt;br&gt; We extend the well-known multidimensional \gls{rimax} parameter estimation framework by including proper frequency responses. The advantage reveals most clearly with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays. It breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence an extended target in radar terminology) we propose a model that assigns a short delay spread, respectively a frequency response to the propagation path that associates itto the respective object.&lt;br&gt; &lt;br&gt; We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it to a state-of-the-art method. Additionally, we use synthetic data based on raytracing results and measurements both ranging from \SI{27}{\giga\hertz} up to \SI{33}{\giga\hertz} with known ground truth information and show that the proposed estimator not based on the narrowband assumption delivers better performance for higher relative bandwidths than the conventional \gls{rimax} implementation.&lt;/p&gt;</jats:p

High Resolution Parameter Estimation for Wideband Radio Channel Sounding

&lt;p&gt;Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight and Doppler shift are estimated from observations in frequency, time and space. A maximum likelihood estimation framework allows joint high-resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements.&lt;br&gt; &lt;br&gt; The used model should be as simple as possible since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept orthogonal. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase.&lt;br&gt; &lt;br&gt; We extend the well-known multidimensional \gls{rimax} parameter estimation framework by including proper frequency responses. The advantage reveals most clearly with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays. It breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence an extended target in radar terminology) we propose a model that assigns a short delay spread, respectively a frequency response to the propagation path that associates itto the respective object.&lt;br&gt; &lt;br&gt; We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it to a state-of-the-art method. Additionally, we use synthetic data based on raytracing results and measurements both ranging from \SI{27}{\giga\hertz} up to \SI{33}{\giga\hertz} with known ground truth information and show that the proposed estimator not based on the narrowband assumption delivers better performance for higher relative bandwidths than the conventional \gls{rimax} implementation.&lt;/p&gt;</jats:p