arXiv subjects

Stefan Schwarz

Publications and source records attributed to Stefan Schwarz.

At least 37 records · Page 2Linked to original sources

arXiv2022

Prediction and validation of the strongly modulated forced response of two beams undergoing frictional impacts

We consider two cantilevered beams undergoing frictional impacts at the free end. The beams are designed to be of similar geometry so that they have distinct but close natural frequencies. Under harmonic base excitation near the primary resonance with the higher-frequency fundamental bending mode, the system shows a strongly modulated non-periodic response. The purpose of this work is to analyze to what extent the non-periodic vibro-impact dynamics can be predicted. To this end, we use a recently developed modeling and simulation approach. The approach relies on component mode synthesis, the massless boundary concept and an appropriate time stepping scheme. Unilateral contact and dry friction are modeled as set-valued laws and imposed locally within the spatially resolved contact area. A linear model updating is carried out based on the natural frequencies and damping ratios identified in the regime without impacts. The nonlinear simulation of the steady-state response to forward and backward stepped sine excitation is compared against measurements. The results are in very good agreement, especially in the light of the uncertainty associated with the observed material loss in the contact region and the nonlinear behavior of the clamping.

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arXiv2022

Impact of Channel Correlation on Subspace-Based Activity Detection in Grant-Free NOMA

In this paper, we consider the problem of activity detection in grant-free code-domain non-orthogonal multiple access (NOMA). We focus on performing activity detection via subspace methods under a setup where the data and pilot spreading signatures are of different lengths, and consider a realistic frame-structure similar to existing mobile networks. We investigate the impact of channel correlation on the activity detection performance; first, we consider the case where the channel exhibits high correlation in time and frequency and show how it can heavily deteriorate the performance. To tackle that, we propose to apply user-specific masking sequences overlaid on top of the pilot signatures. Second, we consider the other extreme with the channel being highly selective, and show that it can also negatively impact the performance. We investigate possible pilots' reallocation strategies that can help reduce its impact.

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arXiv2022

Attention Aided CSI Wireless Localization

Deep neural networks (DNNs) have become a popular approach for wireless localization based on channel state information (CSI). A common practice is to use the raw CSI in the input and allow the network to learn relevant channel representations for mapping to location information. However, various works show that raw CSI can be very sensitive to system impairments and small changes in the environment. On the contrary, hand-designing features may hinder the limits of channel representation learning of the DNN. In this work, we propose attention-based CSI for robust feature learning. We evaluate the performance of attended features in centralized and distributed massive MIMO systems for ray-tracing channels in two non-stationary railway track environments. By comparison to a base DNN, our approach provides exceptional performance.

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arXiv2021

Learning-based Remote Radio Head Selection and Localization in Distributed Antenna System

In this work, we consider estimating user positions in a spatially distributed antenna system (DAS) from the uplink channel state information (CSI). However, with the increased number of remote radio heads (RRHs), collecting CSI at a central unit (CU) can significantly increase the fronthaul overhead and computational complexity of the CU. This problem can be mitigated by selecting a subset of RRHs. Thus, we present a deep learning-based approach to select a subset of RRHs for wireless localization. We employ an RRH selection layer that is jointly trained with the rest of the network and learn the model parameters as well as the set of selected RRHs. We show that the selection strategy comes at a relatively small cost of localization performance. Nonetheless, by comparison to a trivial approach based on the maximization of the channel gain, we show that the proposed method leads to significant performance gains in a propagation environment dominated by non-line-of-sight.

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arXiv2021

Towards Scalable Uncertainty Aware DNN-based Wireless Localisation

Existing deep neural network (DNN) based wireless localization approaches typically do not capture uncertainty inherent in their estimates. In this work, we propose and evaluate variational and scalable DNN approaches to measure the uncertainty as a result of changing propagation conditions and the finite number of training samples. Furthermore, we show that data uncertainty is sufficient to capture the uncertainty due to non-line-of-sight (NLOS) and, model uncertainty improves the overall reliability. To assess the robustness due to channel conditions and out-of-set regions, we evaluate the methods on challenging massive multiple-input multiple-output (MIMO) scenarios.

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arXiv2021

RIS-Assisted Code-Domain MIMO-NOMA

We consider the combination of uplink code-domain non-orthogonal multiple access (NOMA) with massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RISs). We assume a setup in which the base station (BS) is capable of forming beams towards the RISs under line-of-sight conditions, and where each RIS is covering a cluster of users. In order to support multi-user transmissions within a cluster, code-domain NOMA via spreading is utilized. We investigate the optimization of the RIS phase-shifts such that a large number of users is supported. As it turns out, it is a coupled optimization problem that depends on the detection order under interference cancellation and the applied filtering at the BS. We propose to decouple those variables by using sum-rate optimized phase-shifts as the initial solution, allowing us to obtain a decoupled estimate of those variables. Then, in order to determine the final phase-shifts, the problem is relaxed into a semidefinite program that can be solved efficiently via convex optimization algorithms. Simulation results show the effectiveness of our approach in improving the detectability of the users.

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arXiv2021

Outage Analysis of Uplink IRS-Assisted NOMA under Elements Splitting

In this paper, we investigate the outage performance of an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) uplink, in which a group of the surface reflecting elements are configured to boost the signal of one of the user equipments (UEs), while the remaining elements are used to boost the other UE. By approximating the received powers as Gamma random variables, tractable expressions for the outage probability under NOMA interference cancellation are obtained. We evaluate the outage over different splits of the elements and varying pathloss differences between the two UEs. The analysis shows that for small pathloss differences, the split should be chosen such that most of the IRS elements are configured to boost the stronger UE, while for large pathloss differences, it is more beneficial to boost the weaker UE. Finally, we investigate a robust selection of the elements' split under the criterion of minimizing the maximum outage between the two UEs.

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arXiv2021

Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

Deploying antenna arrays with an asymptotically large aperture will be central to achieving the theoretical gains of massive MIMO in beyond-5G systems. Such extra-large MIMO (XL-MIMO) systems experience propagation conditions which are not typically observed in conventional massive MIMO systems, such as spatial non-stationarities and near-field propagation. Moreover, standard precoding schemes, such as zero-forcing (ZF), may not apply to XL-MIMO transmissions due to the prohibitive complexity associated with such a large-scale scenario. We propose two novel precoding schemes that aim at reducing the complexity without losing much performance. The proposed schemes leverage a plane-wave approximation and user grouping to obtain a low-complexity approximation of the ZF precoder. Our simulation results show that the proposed schemes offer a possibility for a performance and complexity trade-off compared to the benchmark schemes.

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arXiv2020

Analysis of Uplink IRS-Assisted NOMA under Nakagami-m Fading via Moments Matching

This letter investigates the uplink outage performance of intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA). We consider the general case where all users have both direct and reflection links, and all links undergo Nakagami-m fading. We approximate the received powers of the NOMA users as Gamma random variables via moments matching. This allows for tractable expressions of the outage under interference cancellation (IC), while being flexible in modeling various propagation environments. Our analysis shows that under certain conditions, the presence of an IRS might degrade the performance of users that have dominant line-of-sight (LOS) to the base station (BS), while users dominated by non-line-of-sight (NLOS) will always benefit from it.

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arXiv2020

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this paper, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.

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arXiv2020

Low-Complexity Massive MIMO Tensor Precoding

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channel vectors can be factorized as a tensor product between two lower dimensional subspaces. Based on this result, we formulate tensor maximum ratio transmit and zero-forcing precoders. We show that the proposed tensor precoders exhibit lower computational complexity and require less instantaneous channel state information than their linear counterparts. Finally, we present computer simulations that demonstrate the applicability of the proposed tensor precoders in practical communication scenarios.

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arXiv2019

Double-Sided Massive MIMO Transceivers for MmWave Communications

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.

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arXiv2018

Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.

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arXiv2018

Versatile Mobile Communications Simulation: The Vienna 5G Link Level Simulator

Research and development of mobile communications systems require a detailed analysis and evaluation of novel technologies to further enhance spectral efficiency, connectivity and reliability. Due to the exponentially increasing demand of mobile broadband data rates and challenging requirements for latency and reliability, mobile communications specifications become increasingly complex to support ever more sophisticated techniques. For this reason, analytic analysis as well as measurement based investigations of link level methods soon encounter feasibility limitations. Therefore, computer aided numeric simulation is an important tool for investigation of wireless communications standards and is indispensable for analysis and developing future technologies. In this contribution, we introduce the Vienna 5G Link Level Simulator, a Matlab-based link level simulation tool to facilitate research and development of 5G and beyond mobile communications. Our simulator enables standard compliant setups according to 4G Long Term Evolution, 5G new radio and even beyond, making it a very flexible simulation tool. Offered under an academic use license to fellow researchers it considerably enhances reproducibility in wireless communications research. We give a brief overview of our simulation platform and introduce unique features of our link level simulator in more detail to outline its versatile functionality.

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arXiv2018

Better than Rician: Modelling millimetre wave channels as Two-Wave with Diffuse Power

This contribution provides experimental evidence for the two-wave with diffuse power (TWDP) fading model. We have conducted two indoor millimetre wave measurement campaigns with directive horn antennas at both link ends. One horn antenna is mounted in a corner of our laboratory, while the other is steerable and scans azimuth and elevation. Our first measurement campaign is based on scalar network analysis with 7 GHz of bandwidth. Our second measurement campaign obtains magnitude and phase information, additionally sampled directionally at several positions in space. %This second measurement campaign is limited to 2 GHz bandwidth. We apply Akaike's information criterion to decide whether Rician fading sufficiently explains the data or the generalized TWDP fading model is necessary. Our results indicate that the TWDP fading hypothesis is favoured over Rician fading in situations where the steerable antenna is pointing towards reflecting objects or is slightly misaligned at line-of-sight. We demonstrate TWDP fading in several different domains, namely, frequency, space, and time.

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arXiv2017

Cellular Network Architectures for the Society in Motion

Due to rising mobility worldwide, a growing number of people utilizes cellular network services while on the move. Persistent urbanization trends raise the number of daily commuters, leading to a situation where telecommunication requirements are mainly dictated by two categories of users: 1) Static users inside buildings, demanding instantaneous and virtually bandwidth unlimited access to the Internet and Cloud services; 2) moving users outside, expecting ubiquitous and seamless mobility even at high velocity. While most work on future mobile communications is motivated by the first category of users, we outline in this article a layered cellular network architecture that has the potential to efficiently support both user groups simultaneously. We deduce novel transceiver architectures and derive research questions that need to be tackled to effectively maintain wireless connectivity for the envisioned Society in Motion.

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arXiv2015

Latency and Resource Utilization Analysis for V2X Communication over LTE MBSFN Transmission

In this paper, we investigate the performance of LTE Multicast-Broadcast Single-Frequency Networks (MBSFN). LTE-MBSFN is viewed as one of the most promising candidates for vehicular communications which can enhance reliability of vehicular application traffic. This is achieved due to the possibility to efficiently support message exchange in-between vehicles by multicasting information to several vehicles in parallel (point-to-multipoint transmission) employing an Multimedia Broadcast/Multicast Service (MBMS). We investigate two metrics to gauge the performance of MBMS/MBSFN transmissions in comparison with standard unicast transmissions for vehicular communications: latency of packet delivery and overhead caused by vehicular traffic, i.e., network utilization. Additionally, we present technique of prediction of system behaviour and explore the influence of transmission bandwidth and transmission rate on mentioned metrics.

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arXiv2015

Exploring the Physical Layer Frontiers of Cellular Uplink - The Vienna LTE-A Simulator

Communication systems in practice are subject to many technical/technological constraints and restrictions. MIMO processing in current wireless communications, as an example, mostly employs codebook based pre-coding to save computational complexity at the transmitters and receivers. In such cases, closed form expressions for capacity or bit-error probability are often unattainable; effects of realistic signal processing algorithms on the performance of practical communication systems rather have to be studied in simulation environments. The Vienna {LTE-A} Uplink Simulator is a 3GPP {LTE-A} standard compliant link level simulator that is publicly available under an academic use license, facilitating reproducible evaluations of signal processing algorithms and transceiver designs in wireless communications. This paper reviews research results that have been obtained by means of the Vienna LTE-A Uplink Simulator, highlights the effects of Single Carrier Frequency Division Multiplexing (as the distinguishing feature to LTE-A downlink), extends known link adaptation concepts to uplink transmission, shows the implications of the uplink pilot pattern for gathering Channel State Information at the receiver and completes with possible future research directions.

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