Congress and Conference Contributions

Deep learning (DL) methods have been recently proposed for user equipment (UE) localization in wireless communication networks, based on the channel state information (CSI) between a UE and multiple base stations (BSs) in the uplink. With the CSI from the available BSs, UE localization can be performed in different ways. On the one hand, a single neural network (NN) can be trained for the UE localization by considering the CSI from all the available BSs as one overall fingerprint of the user’s location. On the other hand, the CSI at each BS can be used to obtain an estimate of the UE’s position with a separate NN at each BS, and then the position estimates of all BSs are combined to obtain an overall estimate of the UE position. In this work, we show that UE localization with the latter approach can achieve a higher positioning accuracy. We propose to consider the uncertainty in the UE localization at each BS, such that overall UE’s position is determined by combining the position estimates of the different BSs based on the uncertainty at each BS. With this approach, a more reliable position estimate can be obtained in case of variations in the channel.

Service robotics is expected to be one of the central growth industries of this century. The technological key to this lies in the sufficient perception of the environment, even under difficult conditions, because mobile robots have to orientate themselves and navigate without collision under all circumstances. However, the safe detection of especially descending stairs is a big challenge so far. In this paper, the results of descending staircase detection using UWB radar are shown. The individual steps of the signal processing from signal preparation to multi-target tracking are briefly explained and an outlook is given on how to classify a staircase based on the results. For all investigated stairs the edge of the first step could be detected reliably from a distance of 1.5 m and multiple steps are distinguishable as well.

In this paper, we investigate the sensitivity of the software-defined radio (SDR) universal software radio peripheral (USRP) X310 with a TwinRX daughterboard to strong interferers. In a conducted test setup, the signal-to-interference-plus-noise ratio (SINR) is measured under the influence of a mock-up Long-Term Evolution (LTE) base station. The frequency of the interferer is varied to analyze the effects over a wide frequency band, whereby a distinction is made between signals inside and outside the intermediate frequency (IF) band. The former can cause clipping at the analog-to-digital converter (ADC), while the latter are to be suppressed in the analog part of the software-defined radio. Here, leakage and higher-order mixing products make the receiver sensitive to spurious frequencies, whereby the performance degradation is particularly noticeable near the first intermediate frequency (IF1) of 1.25 GHz. At this point, even an interference signal with −28 dBm peak envelope power (PEP) causes an SINR loss of 3 dB.

In the present paper we introduce simultaneous multi-band measurements at sub-6 GHz and two mm-wave bands with the objective of characterizing propagation for multi-band channel modelling purposes in industry scenarios. The marginal power profiles show that the dominant scatterers are common in the different frequencies. In addition, a relation of decreasing average delay and angular spreads with increasing frequency is observed. The different measured parameters are contrasted with the 3GPP model for indoor factories.

In the present paper we introduce the empirical results of measurements with an over-the-air based propagation artifact for verification and validation of sub-THz and THz channel sounders and parameter estimation algorithms. This experiment produces a fixed number of multipath components with traceable propagation properties in the different domains that can be used to test resolution and performance. Because of the inherent characteristics of the measurement hardware, we have introduced an adaptation on a parametric high resolution estimation algorithm to account the imperfections of the channel sounder. The results have shown to account for a relative good performance of the sounder and the tested parametric and non-parametric estimation algorithms.