arXiv subjects

Markus Hofer

Publications and source records attributed to Markus Hofer.

31 records · Page 2Linked to original sources

arXiv2022

Towards Cell-Free Massive MIMO: A Measurement-Based Analysis

Cell-free widely distributed massive multiple-input multiple-output (MIMO) systems utilize radio units spread out over a large geographical area. The radio signal of a user equipment (UE) is coherently detected by a subset of radio units (RUs) in the vicinity of the UE and processed jointly at the nearest baseband processing unit (BPU). This architecture promises two orders of magnitude less transmit power, spatial focusing at the UE position for high reliability, and consistent throughput over the coverage area. All these properties have been investigated so far from a theoretical point of view. To the best of our knowledge, this work presents the first empirical radio wave propagation measurements in the form of time-variant channel transfer functions for a linear, widely distributed antenna array with 32 single antenna RUs spread out over a range of 46.5 m. The large aperture allows for valuable insights into the propagation characteristics of cell-free systems. Three different co-located and widely distributed RU configurations and their properties in an urban environment are analyzed in terms of time-variant delay-spread, Doppler spread, path loss and the correlation of the local scattering function over space. For the development of 6G cell-free massive MIMO transceiver algorithms, we analyze properties such as channel hardening, channel aging as well as the signal to interference and noise ratio (SINR). Our empirical evidence supports the promising claims for widely distributed cell-free systems.

eess.SP↗

arXiv2021

Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modelling, and Hardware-in-the-Loop Evaluation

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than $3.6\,\mathrm{dB}$, $78\,\mathrm{ns}$, and $52\,\mathrm{Hz}$, respectively, for $80\%$ of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for $86\%$ of the simulation duration.

eess.SP↗

arXiv2020

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we present the COVID-19 Early Warning System (CovEWS), a clinical risk scoring system for assessing COVID-19 related mortality risk. CovEWS provides continuous real-time risk scores for individual patients with clinically meaningful predictive performance up to 192 hours (8 days) in advance, and is automatically derived from patients' electronic health records (EHRs) using machine learning. We trained and evaluated CovEWS using de-identified data from a cohort of 66430 COVID-19 positive patients seen at over 69 healthcare institutions in the United States (US), Australia, Malaysia and India amounting to an aggregated total of over 2863 years of patient observation time. On an external test cohort of 5005 patients, CovEWS predicts COVID-19 related mortality from $78.8\%$ ($95\%$ confidence interval [CI]: $76.0$, $84.7\%$) to $69.4\%$ ($95\%$ CI: $57.6, 75.2\%$) specificity at a sensitivity greater than $95\%$ between respectively 1 and 192 hours prior to observed mortality events - significantly outperforming existing generic and COVID-19 specific clinical risk scores. CovEWS could enable clinicians to intervene at an earlier stage, and may therefore help in preventing or mitigating COVID-19 related mortality.

stat.AP↗

arXiv2019

Sparsity in the Delay-Doppler Domain for Measured 60 GHz Vehicle-to-Infrastructure Communication Channels

We report results from millimeter wave vehicle-to-infrastructure (V2I) channel measurements conducted on Sept. 25, 2018 in an urban street environment, down-town Vienna, Austria. Measurements of a frequency-division multiplexed multiple-input single-output channel have been acquired with a time-domain channel sounder at 60 GHz with a bandwidth of 100 MHz and a frequency resolution of 5 MHz. Two horn antennas were used on a moving transmitter vehicle: one horn emitted a beam towards the horizon and the second horn emitted an elevated beam at 15-degrees up-tilt. This configuration was chosen to assess the impact of beam elevation on V2I communication channel characteristics: propagation loss and sparsity of the local scattering function in the delay-Doppler domain. The measurement results within urban speed limits show high sparsity in the delay-Doppler domain.

eess.SP↗

arXiv2018

Iterative Detection for Orthogonal Precoding in Doubly Selective Channels

Ultra-reliable wireless communication links require the utilization of all diversity sources of a wireless communication channel. Hadani et al. propose a two dimensional discrete symplectic Fourier transform (DSFT) as orthogonal pre-coder for a time-frequency modulation scheme. In this paper we explore \emph{general} orthogonal precoding (OP) and its performance in time- and frequency-selective channels. We show that iterative parallel interference cancellation (PIC) and iterative channel estimation methods can be used for the detection of OP. A scalar signal model for OP transmission is obtained by PIC. Based on this signal model, we can prove that all constant modulus sequences, e.g. the DSFT basis functions or Walsh-Hadamard sequences, lead to the same performance for OP. We validate our receiver structure by numerical link level simulations of a vehicle-to-vehicle communication link with a relative velocity of $0\ldots200\,\text{km/h}$. We demonstrate that OP achieves a gain of about $4.8\,\text{dB}$ if compared to orthogonal frequency division multiplexing at a bit error rate of $10^{-4}$. Our performance results for coded OP are the best results for a fully documented receiver architecture, published so far.

cs.IT↗

arXiv2017

Low-Complexity Equalization for Orthogonal Time and Frequency Signaling (OTFS)

Recently, a new precoding technique called orthogonal time-frequency signaling (OTFS) has been proposed for time- and frequency-selective communication channels. OTFS precodes a data frame with a complete set of spreading sequences and transmits the results via orthogonal frequency division multiplexing (OFDM). OTFS uses two dimensional (2D) linear spreading sequences in time and frequency which are the basis functions of a symplectic Fourier transform. OTFS allows the utilization of time- and frequency-diversity but requires maximum likelihood decoding to achieve full diversity. In this paper we show performance results of a low-complexity equalizer using soft-symbol feedback for interference cancellation after an initial minimum-mean square error equalization step. Performance results for an implementation in the delay-Doppler domain and in the time-frequency domain are compared. With our equalizer, OTFS achieves a gain of 5dB compared to OFDM for a bit error rate of $10^{-4}$ and a velocity of $200\,\text{km/h}$.

cs.IT↗

arXiv2017

A time-variant channel prediction and feedback framework for interference alignment

In interference channels, channel state information (CSI) can be exploited to reduce the interference signal dimensions and thus achieve the optimal capacity scaling, i.e. degrees of freedom, promised by the interference alignment technique. However, imperfect CSI, due to channel estimation error, imperfect CSI feedback and time selectivity of the channel, lead to a performance loss. In this work, we propose a novel limited feedback algorithm for single-input single-output interference alignment in time-variant channels. The feedback algorithm encodes the channel evolution in a small number of subspace coefficients, which allow for reduced-rank channel prediction to compensate for the channel estimation error due to time selectivity of the fading process and feedback delay. An upper bound for the rate loss caused by feedback quantization and channel prediction is derived. Based on this bound, we develop a dimension switching algorithm for the reduced-rank predictor to find the best tradeoff between quantization- and prediction-error. Besides, we characterize the scaling of the required number of feedback bits in order to decouple the rate loss due to channel quantization from the transmit power. Simulation results show that a rate gain over the traditional non-predictive feedback strategy can be secured and a 60% higher rate is achieved at 20 dB signal-to-noise ratio with moderate mobility.

cs.IT↗

arXiv2014

On corner avoidance of $\boldsymbol β$-adic Halton sequences

We consider the corner avoiding property of $s$-dimensional $\boldsymbol β$-adic Halton sequences. After extending this class of point sequences in an intuitive way, we show that the hyperbolic distance between each element of the sequence and the closest corner of $[0,1)^s$ is $\mathcal{O}\left(\frac{1}{N^{s/2+ε}}\right)$, where $N$ denotes the index of the element. In our proof we use tools from Diophantine analysis, more precisely, we apply Schmidt's Subspace Theorem.

math.NT↗

arXiv2014

Optimal Bounds for Integrals with Respect to Copulas and Applications

We consider the integration of two-dimensional, piecewise constant functions with respect to copulas. By drawing a connection to linear assignment problems, we can give optimal upper and lower bounds for such integrals and construct the copulas for which these bounds are attained. Furthermore, we show how our approach can be extended in order to approximate extremal values in very general situations. Finally, we apply our approximation technique to problems in financial mathematics and uniform distribution theory, such as the model-independent pricing of first-to-default swaps.

math.OC↗

arXiv2013

A central limit theorem for Latin hypercube sampling with dependence and application to exotic basket option pricing

We consider the problem of estimating $\mathbb{E} [f(U^1, \ldots, U^d)]$, where $(U^1, \ldots, U^d)$ denotes a random vector with uniformly distributed marginals. In general, Latin hypercube sampling (LHS) is a powerful tool for solving this kind of high-dimensional numerical integration problem. In the case of dependent components of the random vector $(U^1, \ldots, U^d)$ one can achieve more accurate results by using Latin hypercube sampling with dependence (LHSD). We state a central limit theorem for the $d$-dimensional LHSD estimator, by this means generalising a result of Packham and Schmidt. Furthermore we give conditions on the function $f$ and the distribution of $(U^1, \ldots, U^d)$ under which a reduction of variance can be achieved. Finally we compare the effectiveness of Monte Carlo and LHSD estimators numerically in exotic basket option pricing problems.

q-fin.CP↗

arXiv2013

Ergodic properties of β-adic Halton sequences

We investigate a parametric extension of the classical s-dimensional Halton sequence, where the bases are special Pisot numbers. In a one- dimensional setting the properties of such sequences have already been in- vestigated by several authors [5, 8, 23, 28]. We use methods from ergodic theory to in order to investigate the distribution behavior of multidimen- sional versions of such sequences. As a consequence it is shown that the Kakutani-Fibonacci transformation is uniquely ergodic.

math.NT↗

arXiv2012

On the uniform distribution modulo 1 of multidimensional LS-sequences

Ingrid Carbone introduced the notion of so-called LS-sequences of points, which are obtained by a generalization of Kakutani's interval splitting procedure. Under an appropriate choice of the parameters $L$ and $S$, such sequences have low discrepancy, which means that they are natural candidates for Quasi-Monte Carlo integration. It is tempting to assume that LS-sequences can be combined coordinatewise to obtain a multidimensional low-discrepancy sequence. However, in the present paper we prove that this is not always the case: if the parameters $L_1,S_1$ and $L_2,S_2$ of two one-dimensional low-discrepancy LS-sequences satisfy certain number-theoretic conditions, then their two-dimensional combination is not even dense in $[0,1]^2$.

math.NT↗

arXiv2012

Probabilistic discrepancy bound for Monte Carlo point sets

By a profound result of Heinrich, Novak, Wasilkowski, and Wo{ź}niakowski the inverse of the star-discrepancy $n^*(s,\ve)$ satisfies the upper bound $n^*(s,\ve) \leq c_{\mathrm{abs}} s \ve^{-2}$. This is equivalent to the fact that for any $N$ and $s$ there exists a set of $N$ points in $[0,1]^s$ whose star-discrepancy is bounded by $c_{\mathrm{abs}} s^{1/2} N^{-1/2}$. The proof is based on the observation that a random point set satisfies the desired discrepancy bound with positive probability. In the present paper we prove an applied version of this result, making it applicable for computational purposes: for any given number $q \in (0,1)$ there exists an (explicitly stated) number $c(q)$ such that the star-discrepancy of a random set of $N$ points in $[0,1]^s$ is bounded by $c(q) s^{1/2} N^{-1/2}$ with probability at least $q$, uniformly in $N$ and $s$.

math.NA↗