In acoustic underwater communication and sonar applications, obstacles inside and outside the line-of-sight (LOS) affect signal propagation. Both reflection and diffraction occur in underwater communication and measurement systems due to these obstacles. To the best of our knowledge, the influence of diffraction and reflection is neither described nor modeled for finite pulses yet. We propose and develop a multipath propagation model for spectral diffraction components and phase information of the received signal based on knife-edge diffraction together with reflections, transmission effects, and backscatter. This paper designs a short range underwater ultrasonic experimental system composed of an ultrasonic transceiver with wideband pulses and advanced spectral signal processing. We evaluate our proposed model with measurements made in a water tank with an obstacle moved between the transmitter and receiver. When the model includes all major propagation components and effects, it achieves an accuracy for localization of 97% of the results in the range of twice the obstacle diameter in our test setup.

With the fifth-generation new radio (5G NR) being ready for deployment and with the possibility of acquiring private licenses, 5G is ready for campus deployment in industry and academia. As industry and academia do not have their own LTE networks, especially 5G standalone (SA) implementations gain importance. However, SA deployments include all components from the user equipment to radio units and core functionality in a large distributed system to work properly. Since commercial systems are costly, it is worth considering open-source implementations. We face the following challenges: many open-source projects are yet in the development phase, it is challenging to deploy 5G systems because hardware and software continuously evolve, documentation is incomplete, and 5G SA is a new technology. A small community runs a continuously changing deployment in a development branch. From the lessons learned, our contribution to the community is to provide a well-documented deployment based on existing tutorials and sometimes in code research and configuration file tests like try and error. For evaluation and proof of concept, we built a 5G system at the Technische Hochschule Lübeck - University of Applied Sciences with the provision of all information to deploy the system step by step and perform integration tests. We provide a complete solution for low costs of 10k EUR with off-the-shelf components and provide basic connectivity and iperf measurement results for latency and throughput as a reference for own deployments.

Device-free localization (DFL) systems exploit changes in the radio frequency channel by measuring, for example, the channel impulse response (CIR), to detect and localize obstacles within a target area. However, due to a lack of well-defined interfaces, missing modularization, as well as complex system configuration, it is difficult to deploy DFL systems outside of laboratory setups. This paper focused on the system view and the challenges that come with setting up a DFL system in an indoor environment. We propose MA-RTI, a modular DFL system that is easy to set up, and which utilizes a multipath-assisted (MA) radio-tomographic imaging (RTI) algorithm. To achieve a modular DFL system, we proposed and implemented an architectural model for DFL systems. For minimizing the configuration overhead, we applied a 3D spatial model, that helps in placing the sensors and calculating the required calibration parameters. Therefore, we configured the system solely with idle measurements and a 3D spatial model. We deployed such a DFL system and evaluated it in a real-world office environment with four sensor nodes. The radio technology was ultra-wideband (UWB) and the corresponding signal measurements were CIRs. The DFL system operated with CIRs that provided a sub-nanosecond time-domain resolution. After pre-processing, the update rate was approximately 46 Hz and it provided a localization accuracy of 1.0 m in 50% of all cases and 1.8 m in 80% of all cases. MA fingerprinting approaches lead to higher localization accuracy, but require a labor-intensive training phase.

Among other methods, UWB-based multi-anchor localization systems have been established for industrial indoor localization systems. However, multi-anchor systems have high costs and installation effort. By exploiting the multipath propagation of the UWB signal, the infrastructure and thus the costs of conventional systems can be reduced. Our UWB Single-Anchor Localization System (SALOS) successfully pursues this approach. The idea is to create a localization system with sophisticated signal modeling. Therefore, measured reference, like fingerprinting or training, is not required for position estimation. Although SALOS has already been implemented and tested successfully in an outdoor scenario with multipath propagation, it has not yet been evaluated in an indoor environment with challenging and hardly predictable multipath propagation. For this purpose, we have developed new algorithms for the existing hardware, mainly a three-dimensional statistical multipath propagation model for arbitrary spatial geometries. The signal propagation between the anchor and predefined candidate points for the tag position is modeled in path length and complex-valued receive amplitudes. For position estimation, these modeled signals are combined to multiple sets and compared to UWB measurements via a similarity metric. Finally, a majority decision of multiple position estimates is performed. For evaluation, we implement our localization system in a modular fashion and install the system in a building. For a fixed grid of 20 positions, the localization is evaluated in terms of position accuracy. The system results in correct position estimations for more than 73% of the measurements.

Among others, UWB-based multi anchor localization systems have been established for industrial indoor localization systems. However, multi anchor systems have high costs and installation effort. By exploiting the multipath propagation of the UWB signal, the infrastructure and thus the costs of conventional systems can be reduced. Our UWB Single Anchor Localization System (SALOS) successfully pursues this approach. The idea behind SALOS is to create a localization system with a sophisticated signal modeling. Therefore, neither measured reference like fingerprinting nor training is required for position estimation. Although SALOS has been implemented and tested successfully already in an outdoor scenario with multipath propagation, it has not yet been evaluated in indoor environment with challenging and hardly predictable multipath propagation. For this purpose, we have developed new algorithms for the existing hardware mainly a three-dimensional statistical multipath propagation model for arbitrary spatial geometries. The signal propagation between the anchor and predefined candidate points for the tag position is modeled in path length and complex-valued receive amplitudes. For position estimation, these modeled signals are combined to multiple sets and compared to UWB measurements via a similarity metric. In the end, a majority decision of multiple position estimates is performed. For evaluation, we implement SALOS in a modular fashion and install the system in a building. For a fixed grid of 20 positions, SALOS is evaluated in terms of position accuracy. The system results in correct position estimations for more than 73% of the measurements.

In this paper, we present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance and power consumption of our system with measurements. We show that our system achieves acceptable data rates, is very space and energy efficient with an average power consumption of 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work.