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The 3rd Generation Partnership Project (3GPP) aims to integrate non-terrestrial networks (NTNs) with terrestrial 5G networks. In this context, prototyping and testing are essential to demonstrate the readiness of new 5G-NTN releases. However, commercial 5G protocol stack implementations are usually costly and closed solutions, which makes it challenging for universities and nonprofit research institutions to adapt and customize them for 5G-NTN testing and development purposes. Software-defined radio testbeds that utilize open-source software implementations are an attractive alternative. In this article, we describe our protocol stack adaptation of an open-source implementation for 5G-NTN based on OpenAirInterface (OAI), which runs on commodity hardware. We evaluate the performance of our 5G-NTN in various layers in real-world environments with a transparent geostationary satellite. We validate our testbed with a variety of Internet applications. These include streaming video, web browsing, and voice over IP. The results indicate that the current implementation utilizing the 5 MHz bandwidth is reliable over several hours and achieves both downlink and uplink speeds of 3.6 Mb/s while maintaining a typical geostationary latency of approximately 530 ms. The tests demonstrate that our 5G-NTN testbed is an reliable alternative to costly commercial implementations.

The calculation of magnetically induced vibrations in the stator of an electric machine involves, on the one hand, the determination of the magnetic forces originating from the air gap field and, on the other hand, the calculation of the mechanical eigenmodes and eigenfrequencies. This work presents a new method for the mechanical modelling of the stator using shell elements in 3D, as an extension of a recently published method using beam elements in 2D. It is applied to the case of a hydro generator with a complex mechanical construction, including the frame, and the results are validated by measurements performed on a $55\,\text {MVA}$ machine. It is shown that the developed method allows a time-efficient calculation of the eigenmodes and eigenfrequencies, providing an advantageous alternative to the classical analytical and finite element (FE) methods typically used for this purpose. This outcome is particularly relevant and applicable to the design and optimisation of large electric machines, as well as troubleshooting vibration problems in existing machines.

Brain-heart interactions (BHI) are critical for generating and processing emotions, including anxiety. Understanding specific neural correlates would be instrumental for greater comprehension and potential therapeutic interventions of anxiety disorders. While prior work has implicated the pontine structure as a central processor in cardiac regulation in anxiety, the distributed nature of anxiety processing across the cortex remains elusive. To address this, we performed a whole-brain-heart analysis using the full frequency directed transfer function to study resting-state spectral differences in BHI between high and low anxiety groups undergoing fMRI scans. Our findings revealed a hemispheric asymmetry in low-frequency interplay (0.05 Hz - 0.15 Hz) characterized by ascending BHI to the left insula and descending BHI from the right insula. Furthermore, we provide evidence supporting the “pacemaker hypothesis”, highlighting the pons’ function in regulating cardiac activity. Higher frequency interplay (0.2 Hz - 0.4Hz) demonstrate a preference for ascending interactions, particularly towards ventral prefrontal cortical activity in high anxiety groups, suggesting the heart’s role in triggering a cognitive response to regulate anxiety. These findings highlight the impact of anxiety on BHI, contributing to a better understanding of its effect on the resting-state fMRI signal, with further implications for potential therapeutic interventions in treating anxiety disorders.

Although AI algorithms and applications become more and popular in the healthcare sector, only few institutions have an operational AI strategy. Identifying the best suited processes for ML algorithm implementation and adoption is a big challenge. Also, raising human confidence in AI systems is elementary to building trustworthy, socially beneficial and responsible AI. A commonly agreed AI auditing framework that provides best practices and tools could help speeding up the adoption process. In this paper, we first highlight important concepts in the field of AI auditing and then restructure and subsume them into an ML auditing core criteria catalog. We conducted a scoping study where we analyzed sources being associated with the term “Auditable AI” in a qualitative way. We utilized best practices from Mayring (2000), Miles and Huberman (1994), and Bortz and Döring (2006). Based on referrals, additional relevant white papers and sources in the field of AI auditing were also included. The literature base was compared using inductively constructed categories. Afterwards, the findings were reflected on and synthesized into a resulting ML auditing core criteria catalog. The catalog is grouped into the categories: Conceptual Basics, Data & Algorithm Design and Assessment Metrics. As a practical guide, it consists of 30 questions developed to cover the mentioned categories and to guide ML implementation teams. Our consensus-based ML auditing criteria catalog is intended as a starting point for the development of evaluation strategies by specific stakeholders. We believe it will be beneficial to healthcare organizations that have been or will start implementing ML algorithms. Not only to help them being prepared for any upcoming legally required audit activities, but also to create better, well-perceived and accepted products. Potential limitations could be overcome by utilizing the proposed catalog in practice on real use cases to expose gaps and to further improve the catalog. Thus, this paper is seen as a starting point towards the development of a framework, where essential technical components can be specified.

Quantum machine learning (QML) models promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional deep learning (DL) techniques due to their expressive power via their local effective dimension. There are, however, two main challenges regardless of the promised quantum advantage. Currently available quantum bits (qubits) are very small in number, whereas real-world datasets are characterized by hundreds of high-dimensional elements (i.e., features). In addition, there is not a single unified approach for embedding real-world high-dimensional datasets in a limited number of qubits. Some real-world datasets are too small for training intricate QML networks. Hence, to tackle these two challenges for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets in one go, we employ quantum transfer learning comprising a classical VGG16 layer and a multiqubit QML layer. We use real-amplitude and strongly-entangling N-layer QML networks with and without data reuploading layers as a multiqubit QML layer, and evaluate their expressive power quantified by using their local effective dimension and the lower local effective dimension of a QML network, the better its performance on unseen data. As datasets, we utilize Eurosat and synthetic datasets (i.e., easy-to-classify datasets), and an UC Merced Land use dataset (i.e., a hard-to-classify dataset). Our numerical results show that the strongly-entangling N-layer QML network has a lower local effective dimension than the real-amplitude QML network and outperforms it on the hard-to-classify datasets. In addition, quantum transfer learning helps tackle the two challenges mentioned above for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets.

The angle estimation capabilities of radar sensors are strictly linked to the antenna spacing between the array elements. A larger array aperture corresponds to a smaller beam width or in terms of the angle estimation a higher phase sensitivity. So-called Biomimetic Antenna Arrays (BMAAs) provide the opportunity to shape the angular characteristics with more degrees of freedom. In this work, the concept and the design process of a tunable BMAA are presented. By electronically tuning varactor diodes, such an array can adapt its angular characteristics depending on the direction-of-arrival (DoA) and signal strength of a radar target. The theoretical requirements for the desired task are examined with general S-parameter simulations. Extensive circuit simulations of a proposed architecture unveil a variety of achievable designs of tunable BMAAs. Radar measurements of different implementations in the 77 GHz range and the theoretical analysis are in good agreement. The measurements show, inter alia, that the operational point of maximum biomimetically-increased phase sensitivity can be tuned over a range of 40° around the boresight direction, or that the power losses associated with BMAAs can be decreased by approximately 12 dB for weak radar targets, improving the detection capabilities.

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.

To enhance the capabilities of high-resolution imaging or beamforming, the trend goes towards arrays with high-channel count and large aperture sizes. The operation at increasing mm-wave frequencies exacerbates the consequences of manufacturing tolerances on the array performance, further necessitating the development of high-quality calibrations. Thereby, a large number of array response vectors, addressed in the following as data points, is stored. Typically, one data point is measured at a time for each angle. To reduce the calibration effort, hereby defined as the number of required measurements, a calibration setup employing active calibration targets (ACT) is proposed. This setup allows the storage of a multitude of data points within a single measurement. In addition, this measurement setup is placed in the near-field of the array to relax the requirements on the size of the anechoic chamber. Employing multiple targets makes it challenging to compensate the near-field effects. Therefore, methods are discussed to ensure a consistent quality of the calibration considering the near-field influences from the ACTs. Exemplary measurements deploying an antenna array operating at 78.5 GHz demonstrate that this calibration measurement setup employing three ACTs placed in the near-field of the array reduces the calibration effort by a factor of 3 compared to state-of-the-art setups. The average deviation of the antenna phase relations amounts to 4.1 degree, proving that the presented setup achieves the same calibration quality as state-of-the-art setups.

Satellites enable the provision of global 5G coverage utilizing a customized protocol stack for direct access of 5G devices. This paper reports the results of a field trial based on an Open Source Software (OSS) 5G protocol stack modified for Satellite Communications. 5G New Radio (NR) functions have been adapted to support 3GPP Non-Terrestrial Networks (NTN) standardization for Release 17. Some of the currently discussed enhancements for 5G New Radio (NR) direct access for NTNs were evaluated in this study utilizing a geosynchronous satellite system. Non-Terrestrial Networks (NTN) devices and base station were software-defined solutions communicating via satellite through a standardized 5G NR waveform. The results show that Open Source Software (OSS) implementations running on general purpose platforms greatly support the research and standardization process for 5G-Beyond NTNs. New functions related to NTNs can be evaluated at an early stage over channel emulators or real world satellites to highlight the capabilities of space based 5G-Beyond networks. Therefore, OSS can be a starting point to turn the vision of 6G protocols on-board the satellite into reality.

A closed-form and physics-based compact model is presented for calculating the DC characteristics of Schottky barrier field-effect transistors and dual gated reconfigurable field-effect transistors. The given model calculates the charge-carrier injection over the Schottky barriers. This current is separated into a field emission current, given by charge carriers tunneling through the Schottky barriers and a thermionic emission current, given by charge carriers overcoming the Schottky barriers. The model verification is done by comparing the model results to measurements and TCAD simulations.

Future driver assistance and autonomous driving systems require high-resolution 4D imaging radars that provide detailed and robust information about the vehicle's surroundings, even in poor weather or lighting conditions. In this work, a novel high-resolution radar system with 1728 virtual channels is presented, exceeding the state-of-the-art channel count for automotive radar sensors by a factor of 9. To realize the system, a new mixed feedthrough and distribution network topology is employed for the distribution of the ramp oscillator signal. A multilayer printed circuit board is designed and fabricated with all components assembled on the back side, while the radio frequency signal distribution is on a buried layer and only the antennas are on the front side. The array is optimized to enable both multipleinput multiple-output operation and transmit beamforming. A sparse array with both transmit and receive antennas close to the transceivers is realized to form a 2D array with a large unambiguous region of $130 \mathrm{^{\circ }}$ × $75 \mathrm{^{\circ }}$ with a maximal sidelobe level of $-15 \,\mathrm{{\rm dB}}$. The array features a $3 \,\mathrm{{\rm dB}}$ beamwidth of $0.78 \mathrm{^{\circ }}$ × $3.6 \mathrm{^{\circ }}$ in azimuth and elevation, respectively. Radar measurements in an anechoic chamber show that even the individual peaks of the absorber in the chamber can be detected and separated in the range-angle cut of the 4D radar image. The performance is validated by measurements of a parking lot, where cars, a pedestrian, a fence, and a street lamp can be detected, separated, and estimated correctly in size and position.

For multistatic radar networks in the upper mm-wave range with a large spacing between its radar sensor nodes, a coherent signal distribution is very complex and thus very costly. Hence, it is desirable to generate the mm-wave signals individually for each radar sensor node, i.e., non-coherently. However, multistatic radar networks using a non-coherent signal distribution for its radar sensor nodes are affected by systematic errors and uncorrelated phase noise, which reduces the resolution and the detection performance of these systems. In this article, a novel non-coherent signal synthesis concept based on the direct digital synthesis (DDS) principle is presented for multistatic radar networks. Compared to a signal synthesis using a phase-locked loop (PLL), it is shown that the different phase noise behavior of the DDS is beneficial for bistatic signal paths between the radar sensor nodes. The presented hardware concept is considered and analyzed for three different types of coherency regarding the signal distribution: coherent, quasi-coherent, and incoherent. Measurements with a multiple-input multiple-output (MIMO) radar at $150 \,\mathrm{G}\mathrm{Hz}$ prove that despite a non-coherent signal distribution, it is possible to achieve the same detection and imaging performance as with a fully coherent radar by using a DDS.

Background: Stroke is one of the main causes of disability in the world, causing loss of motor function on mainly one side of the body. A proper assessment of motor function is required to help to direct and evaluate therapy. Assessment is currently performed by therapists using observer-based standardized clinical assessment protocols. Sensor-based technologies can be used to objectively quantify the presence and severity of motor impairments in stroke patients. Methods: In this work, a minimally obstructive distributed inertial sensing system, intended to measure kinematics of the upper extremity, was developed and tested in a pilot study, where 10 chronic stroke subjects performed the arm-related tasks from the Fugl-Meyer Assessment protocol with the affected and non-affected side. Results: The pilot study showed that the developed distributed measurement system was adequately sensitive to show significant differences in stroke subjects' arm postures between the affected and non-affected side. The presence of pathological synergies can be analysed using the measured joint angles of the upper limb segments, that describe the movement patterns of the subject. Conclusion: Features measured by the system vary from the assessed FMA-UE sub-score showing its potential to provide more detailed clinical information.

In this article, we propose a promising approach for the application-oriented content classification of spaceborne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is an increasing demand toward explainable machine learning models as they strive to meet the “right to explanation.” The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labeled data and the dependence on features. In this article, we demonstrate the applicability of a latent Dirichlet allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced, which practically demonstrate the idea of “interpretability” in an explainable machine learning paradigm. LDA discovers the latent structures in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class. Our results show that each class has a distinct topic distribution that represents that particular class. Then these classes can be grouped based on their similarity of topic composition. Both the topic composition and grouping are explainable by domain experts.

While the analysis and understanding of multispectral (i.e., optical) remote sensing images have made considerable progress during the last decades, the automated analysis of synthetic aperture radar (SAR) satellite images still needs some innovative techniques to support nonexpert users in the handling and interpretation of these big and complex data. In this article, we present a survey of existing multispectral and SAR land cover image datasets. To this end, we demonstrate how an advanced SAR image analysis system can be designed, implemented, and verified that is capable of generating semantically annotated classification results (e.g., maps) as well as local and regional statistical analytics such as graphical charts. The initial classification is made based on Gabor features and followed by class assignments (labeling). This is followed by inclusion. This can be accomplished by the inclusion of expert knowledge via active learning with selected examples, and the extraction of additional knowledge from public databases to refine the classification results. Then, based on the generated semantics, we can create new topic models, find typical country-specific phenomena and distributions, visualize them interactively, and present significant examples including confusion matrices. This semiautomated and flexible methodology allows several annotation strategies, the inclusion of dedicated analytics procedures, and can generate broad as well as detailed semantic (multi-)labels for all continents, and statistics or models for selected countries and cities. Here, we employ knowledge graphs and exploit ontologies. These components could already be validated successfully. The proposed methodology can also be adapted to other SAR instruments with different resolutions as well as to multispectral images.

In the upper millimeter wave range above 100 GHz, the effect of array imperfections like manufacturing uncertainties and mutual antenna coupling increases, which leads to deviations from the nominal antenna positions and angle-dependent errors in direction-of-arrival (DoA) estimation. The proposed calibration procedure aims to correct the assumed array response model, based on nominal antenna positions and therefore to improve the measurement accuracy of the array. Typically, the calibration parameters are obtained by interpolating a data set consisting of calibration measurements at known angles. The required number of measurement points in the calibration procedure increases with larger array size, which makes array calibration very time-consuming and costly. In this article, a highly efficient angular array calibration procedure is proposed based on the modal wave expansion technique. After an initial theoretical formulation of the modal wave expansion followed by simulations, it is shown how the limitation of the minimum required number of measurement points in conventional calibration techniques can be eliminated. Experimental results with an antenna array at 150 GHz demonstrate that this novel approach achieves a significant reduction of the number of angular measurement points, especially for large arrays with a high channel count, without degrading the DoA estimation performance.

The estimation of the direction-of-arrival in a coherent multi-channel radar system requires the distribution of the high-frequency local oscillator signal to all hardware channels. Various system concepts with distribution network or feedthrough topologies are established. While distribution networks are bulky and costly, the time delays in systems with a feedthrough topology cause systematic errors in the range evaluation. In this work, a system concept based on an injection-locked feedthrough and the related radar system analysis is presented. The disadvantage of time delays in common feedthrough architectures can be eliminated by a hardware correction using the phase shifting capabilities of the injection-locked oscillator. In addition, two software correction algorithms are presented and analyzed. The performance of the realized system is shown by radar measurements and direction-of-arrival estimations. It is proven that the phase noise of different injection-locked oscillators is fully correlated within the radar system which results in the same detection performance as in state-of-the-art radar systems using a signal distribution network. Thus, the injection-locked feedthrough imaging radar topology is a suitable concept of realizing flexible radar frontends without bulky distribution networks.

Deep-learned variational auto-encoders (VAE) have shown remarkable capabilities for lossy image compression. These neural networks typically employ non-linear convolutional layers for finding a compressible representation of the input image. Advanced techniques such as vector quantization, context-adaptive arithmetic coding and variable-rate compression have been implemented in these auto-encoders. Notably, these networks rely on an end-to-end approach, which fundamentally differs from hybrid, block-based video coding systems. Therefore, signal-dependent encoder optimizations have not been thoroughly investigated for VAEs yet. However, rate-distortion optimized encoding heavily determines the compression performance of state-of-the-art video codecs. Designing such optimizations for non-linear, multi-layered networks requires to understand the relationship between the quantization, the bit allocation of the features and the distortion. Therefore, this paper examines the rate-distortion performance of a variable-rate VAE. In particular, one demonstrates that the trained encoder network typically finds features with a near-optimal bit allocation across the channels. Furthermore, one approximates the relationship between distortion and quantization by a higher-order polynomial, whose coefficients can be robustly estimated. Based on these considerations, the authors investigate an encoding algorithm for the Lagrange optimization, which significantly improves the coding efficiency.

Vehicle-to-infrastructure (V2I) communication is an important enabler for intelligent transportation and rail traffic management systems, which are expected to provide significant road traffic safety and efficiency enhancements. Such systems require a level of dependability of the wireless communication link that can be hard to support by state-of-the-art technologies. Transmissions in the millimeter wave (mmWave) band have the potential to provide sufficient bandwidth to support not only traffic management services, but additionally also web and entertainment applications for car and train passengers. mmWave transmissions require directional antennas at the transmitter and/or receiver to compensate for the significant pathloss and to achieve a practically acceptable maximum coupling loss. Such directional communication is feasible in V2I scenarios, because the mobility of users is confined to streets and roads. In this article, we discuss the dependability of directional mmWave V2I communications under two-ray fading conditions. We review methods for enhancing the dependability of the communication link by exploiting macroscopic spatial diversity and by mitigating interference from other transmitters. We furthermore provide guidelines for a dependable system design under two-ray fading and identify open research questions in this context.

The Fifth Generation (5G) New Radio (NR) Physical Layer (PHY) is designed to successfully address diverse user and service requirements by providing a highly flexible framework. This flexibility is viable through a scalable numerology. Since 5G NR targets to multiplex various applications with different quality of service requirements within the same band, 3rd Generation Partnership Project has introduced a mixed (multi) numerology approach and a mini-slot approach to enhance the adaptability of the PHY. In this contribution, we compare these two approaches focusing on the achievement of low-latency communications. We propose optimization problems that enable to maximize the achievable rate of best effort users, while maintaining latency requirements of low-latency users. Exploiting achievable rate performance as one of the fundamental metrics, we show a comparison of mixed numerology and mini-slot approach in different circumstances. In addition to Cyclic Prefix (CP)-Orthogonal Frequency Division Multiplexing (OFDM), we employ Universal Filtered Multicarrier (UFMC) as a potential beyond 5G technology and show that it achieves an improvement over CP-OFDM. The optimization problems for both mixed numerology and the mini-slot approach are initially given by an integer programming solution. In order to reduce computational complexity for large-scale scenarios, we apply the Dantzig-Wolfe decomposition method, showing that it is possible to achieve the optimal solution with significantly reduced complexity by exploiting the structure of the proposed optimization formulation.

The performance of a multi-user multiple-input multiple-output (MIMO) system is mainly determined by the correlation of user channels. Applying channel models without spatial correlation or spatially inconsistent channel models for performance analysis leads to an under-estimation of spatial correlation and therefore to overly optimistic system performance. This is especially pronounced when the wireless channel is modeled as i.i.d. Rayleigh fading for users in a rich scattering environment, for example, for indoor users. To analyze the spatial channel correlation in massive MIMO systems, we performed wireless channel measurements in three different scenarios. In each scenario, we measure 148 receiver positions, spread over a length of almost 9m. Since the wireless channel statistics change with receiver position, we perform a stationarity analysis. We provide a statistical analysis of the measured channel in terms of amplitude distribution and user-side spatial correlation within the region of stationarity. This analysis shows that, even for a deep-indoor user location, the spatial correlation is significantly higher compared to a Rician channel model with the same K factor. We model the measured channel by means of a Rician channel model and a spatially consistent channel model, which is based on the scenario geometry, to provide an insight for the observed propagation phenomena. Results show that the user correlation is not negligible for massive MIMO in outdoor-to-indoor scenarios. The achievable spectral efficiency with linear precoding is 20% lower compared to the Rician channel model, even for large inter-user distances of 6m.

We propose practical transceiver structures for double-sided massive multiple-input-multiple-output (MIMO) systems. Unlike standard massive MIMO, both transmit and receive sides are equipped with high-dimensional antenna arrays. We leverage the multi-layer filtering architecture and propose novel layered transceiver schemes with practical channel state information requirements to simplify the complexity of our double-sided massive MIMO system. We conduct a comprehensive simulation campaign to investigate the performance of the proposed transceivers under different channel propagation conditions and to identify the most suitable strategy. Our results show that the covariance matrix eigenfilter design at the outer transceiver layer combined with maximum eigenmode transmission precoding/minimum mean square error combining at the inner transceiver layer yields the best achievable sum rate performance for different propagation conditions and multi-user interference levels.

Antennas with a massive amount of elements at one end are among 5G mobile communication key technologies for which spectral efficiency is enhanced by serving many users in parallel over tailored minimally interfering beams. This requires channel models that characterize the propagation environment in both azimuth and elevation. Additionally, the channel model has to capture spatial correlation effects among closely located positions, knowing that the propagation characteristics change gradually over the network area. In order to simulate mobile users or advanced beamforming strategies based on user location or angular information, it is crucial that spatial consistency is included in the applied channel models. This paper introduces a novel model for spatial consistency that is applicable to all prevalent geometry-based stochastic channel models. We provide a detailed explanation of the model and analyze its statistical properties and show its behavior when applied to the 3GPP 3D channel model as an example. To validate our model, we perform extensive ray-tracing simulations and show that our model is in a very good agreement with the statistical channel properties from ray-tracing. Following hypothesis testing over obtained ray-tracing statistics, we are able to parametrize our model for various 3GPP scenarios under LOS and NLOS propagation conditions. Finally, complementary aspects such as simulation complexity are discussed and a guideline on model implementation is provided.

In the area of research on massive multiple-input multiple-output (MIMO), two assumptions on the wireless channel dominate channel modeling. Either, a rich scattering environment is assumed and the channel is modeled as i.i.d. Rayleigh fading, or, a line of sight (LOS) channel is assumed, enabling geometric channel modeling under a farfield assumption. However, either of these assumptions represents an extreme case that is unlikely to be observed in practice. While there is a variety of MIMO channel models in literature, most of them, and even very popular geometry based stochastic channel models, are not spatially consistent. This is especially problematic for technologies in which channel correlation of adjacent users is an important factor, such as massive MIMO. In this work, we introduce a simple but spatially consistent MIMO channel model based on multiple scattering theory. Our proposed channel model allows to adjust the Rician K factor by controlling the number and strength of scattering elements. This allows to perform spatially consistent simulations of wireless communications systems for a large range of scattering environments in between an i.i.d. Rayleigh fading assumption and pure LOS channels. A statistical analysis in terms of the Rician K factor for the introduced model is provided and verified by simulations. By comparison to other channel models, we show that non spatially consistent channel models lead to an underestimation of inter-user correlation and therefore to an overestimation of achievable sum rate.