arXiv subjects

Christoph F. Mecklenbräuker

Publications and source records attributed to Christoph F. Mecklenbräuker.

At least 19 recordsLinked to original sources

arXiv2026

Exploiting Out-of-Band Information for Millimeter-Wave MIMO Channel Estimation: Performance in Static and Dynamic Scenarios

To support the high data rates for latency-critical applications, future wireless systems will employ fully digital beamforming multiple-input multiple-output (MIMO) architectures at millimeter wave (mmWave) frequencies. Moreover, mmWave MIMO deployments will coexist with conventional sub-6 GHz MIMO systems, creating opportunities to exploit out-of-band sub-6 GHz information to enhance channel estimation at mmWave frequencies. In this work, we analyze the pilot-aided channel estimation performance of mmWave MIMO systems under various pilot configurations in both static and dynamic environments. We evaluate the system performance in terms of spectral efficiency (SE) for line-of-sight and non-line-of-sight propagation conditions. Simulation results show that incorporating out-of-band sub-6 GHz information yields notable SE gains in both static and dynamic scenarios.

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arXiv2026

Multi-Band Patch Antenna Array for Out-of-Band Aided Millimeter Wave Communication

Future wireless communication systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands within multi-antenna architectures to meet the increasing demand for high data rates. In such multi-band systems, reliable information obtained from the sub-6 GHz band can be exploited to support communication at mmWave frequencies. To ensure that both systems experience similar multi-path propagation effects, the sub-6GHz and mmWave antenna arrays have to be colocated and precisely aligned. However, such a configuration may adversely alter the radiation characteristics of the arrays, potentially degrading their performance. In this paper, we investigate the impact of positioning a mmWave antenna structure in front of a sub-6 GHz antenna structure. Through both simulations and measurements, we evaluate how the presence of the mmWave structure affects the radiation pattern of the sub-6 GHz one. The results demonstrate that the influence of the mmWave structure on the sub-6 GHz performance is minor, indicating that co-located configurations are feasible with negligible degradation.

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arXiv2026

Performance Analysis of Digital Beamforming mmWave MIMO with Low-Resolution DACs/ADCs

Future wireless communications will rely on multiple-input multiple-output (MIMO) beamforming operating at millimeter wave (mmWave) frequency bands to deliver high data rates. To support flexible spatial processing and meet the demands of latency critical applications, it is essential to use fully digital mmWave MIMO beamforming, which relies on accurate channel estimation. However, ensuring power efficiency in fully digital mmWave MIMO systems requires the use of low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). The reduced resolution of these quantizers introduces distortion in both transmitted and received signals, ultimately degrading system performance. In this paper, we investigate the channel estimation performance of mmWave MIMO systems employing fully digital beamforming with low-resolution quantization, under practical system constraints. We evaluate the system performance in terms of spectral efficiency (SE) and energy efficiency (EE). Simulation results demonstrate that a moderate quantization resolutions of 4-bit per DAC/ADC offers a favorable trade-off between energy consumption and achievable data rate.

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arXiv2026

Time-Varying Rician K-factor in Measured Vehicular Channels at cmWave and mmWave Bands

Future vehicular communication systems will integrate millimeter wave (mmWave) technology to enhance data transmission rates. To investigate the propagation effects and small-scale fading differences between mmWave and conventional centimeter wave (cmWave) bands, multi-band channel measurements have to be conducted. One key parameter to characterize small-scale fading is the Rician K-factor. In this paper, we analyze the time-varying K-factor of vehicle-to-infrastructure (V2I) channels across multiple frequency bands, measured in an urban street environment. Specifically, we investigate three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of 31.25 μs. Furthermore, we analyze the relationship between K-factor and root-mean-square (RMS) delay spread. We show that the Ricean K-factor is similar at different frequency bands and that is correlated with the RMS delay spread.

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arXiv2025

Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches

Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information from the sub-6 GHz band. The first method employs a convolutional neural network (CNN), while the second method utilizes a UNet architecture. We compare these proposed methods against deep-learning methods that rely solely on in-band information and with other state-of-the-art out-of-band aided methods. Simulation results show that our proposed out-of-band aided deep-learning methods outperform existing alternatives in terms of achievable spectral efficiency.

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arXiv2024

Double-Directional V2V Channel Measurement using ReRoMA at 60 GHz

The coordination of vehicles is a crucial element of autonomous driving, as it enhances the efficiency, convenience, and safety of road traffic. In order to fully exploit the capabilities of such coordination, communication with high data rate and low latency is required. It can be reasonably argued that millimeter-wave (mm-wave) vehicle-to-vehicle (V2V) systems are capable of fulfilling the aforementioned requirements. Nevertheless, in order to develop a system that can be deployed in real-world scenarios and to gain an understanding of the various effects of mm-wave propagation, it is necessary to perform radio propagation measurements and to derive radio channel models from them across a range of scenarios and environments. To this end, we have conducted measurement campaigns at 60\,GHz in a variety of situations, including driving in a convoy, driving in opposite direction on a six-lane road, and overtaking. These measurements employ a channel sounder based on ReRoMA, a recently introduced concept that enables the real-time measurement of dynamic double-directional radio channels. The evaluations presented herein encompass key channel parameters, including the path loss (path loss coefficient of approximately 1.9), the root mean square (RMS) delay spread (within a range of 5\,ns to 110\,ns), the angular spreads (in a range of 0.05 to 0.4), the power distribution among multipath components, and the channel stationarity time (multiple seconds).

cs.IT↗

arXiv2024

Channel Estimation for mmWave MIMO using sub-6 GHz Out-of-Band Information

Future wireless multiple-input multiple-output (MIMO) communication systems will employ sub-6 GHz and millimeter wave (mmWave) frequency bands working cooperatively. Establishing a MIMO communication link usually relies on estimating channel state information (CSI) which is difficult to acquire at mmWave frequencies due to a low signal-to-noise ratio (SNR). In this paper, we propose three novel methods to estimate mmWave MIMO channels using out-of-band information obtained from the sub-6GHz band. We compare the proposed channel estimation methods with a conventional one utilizing only in-band information. Simulation results show that the proposed methods outperform the conventional mmWave channel estimation method in terms of achievable spectral efficiency, especially at low SNR and high K-factor.

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arXiv2023

Stationarity Evaluation of High-mobility sub-6 GHz and mmWave non-WSSUS Channels

Analysis and modeling of wireless communication systems are dependent on the validity of the wide-sense stationarity uncorrelated scattering (WSSUS) assumption. However, in high-mobility scenarios, the WSSUS assumption is approximately fulfilled just over a short time period. This paper focuses on the stationarity evaluation of high-mobility multi-band channels. We evaluate the stationarity time, the time over which WSSUS is fulfilled approximately. The investigation is performed over real, measured high-mobility channels for two frequency bands, 2.55 and 25.5 GHz. Furthermore, we demonstrate the influence of the user velocity on the stationarity time. We show that the stationarity time decreases with increased relative velocity between the transmitter and the receiver. Furthermore, we show the similarity of the stationarity regions between sub-6 GHz and mmWave channels. Finally, we demonstrate that the sub-6 GHz channels are characterized by longer stationarity time.

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arXiv2023

Robust and Sparse M-Estimation of DOA

A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss for the circularly symmetric complex Gaussian distribution, the ML-loss for the complex multivariate $t$-distribution (MVT) with $ν$ degrees of freedom, as well as Huber and Tyler loss functions. For Gauss loss, the method reduces to Sparse Bayesian Learning (SBL). The root mean square DOA error of the derived estimators is discussed for Gaussian, MVT, and $ε$-contaminated data. The robust SBL estimators perform well for all cases and nearly identical with classical SBL for Gaussian noise.

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arXiv2022

Evaluation of stationarity regions in measured non-WSSUS 60 GHz mmWave V2V channels

Due to high mobility in multipath propagation environments, vehicle-to-vehicle (V2V) channels are generally time and frequency variant. Therefore, the criteria for wide-sense stationarity (WSS) and uncorrelated scattering (US) are just satisfied over very limited intervals in the time and frequency domains, respectively. We test the validity of these criteria in measured vehicular 60 GHz millimeter wave (mmWave) channels, by estimating the local scattering functions (LSFs) from the measured data. Based on the variation of the LSFs, we define time-frequency stationarity regions, over which the WSSUS assumption is assumed to be fulfilled approximately. We analyze and compare both line-of-sight (LOS) and non-LOS (NLOS) V2V communication conditions. We observe large stationarity regions for channels with a dominant LOS connection, without relative movement between the transmitting and receiving vehicle. In the same measured urban driving scenario, modified by eliminating the LOS component in the post-processing, the channel is dominated by specular components reflected from an overpassing vehicle with a relative velocity of 56 km/h. Here, we observe a stationarity bandwidth of 270 MHz. Furthermore, the NLOS channel, dominated by a single strong specular component, shows a relatively large average stationarity time of 16 ms, while the stationarity time for the channel with a rich multipath profile is much shorter, in the order of 5 ms.

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arXiv2018

Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches

Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by conditional mean squared errors (MSEs), and the basis for the next sensing step is adapted accordingly. For given average sensing time, OAS reduces the MSE compared to non-adaptive schemes, when the signal is sparse. This paper studies the asymptotic performance of Bayesian OAS, for unitarily invariant random projections. For sparse signals, it is shown that OAS with Bayesian recovery and hard adaptation significantly outperforms the minimum MSE bound for non-adaptive sensing. To address implementational aspects, two computationally tractable algorithms are proposed, and their performances are compared against the state-of-the-art non-adaptive algorithms via numerical simulations. Investigations depict that these low-complexity OAS algorithms, despite their suboptimality, outperform well-known non-adaptive schemes for sparse recovery, such as LASSO, with rather small oversampling factors. This gain grows, as the compression rate increases.

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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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arXiv2018

Oversampled Adaptive Sensing

We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior mean-squared error. For sparse Gaussian sources a significant reduction in average sensing time and/or mean-squared error is achieved in comparison to non-adaptive sensing. For compression ratio 3, a sparse 10% Gaussian source and equal average sensing times, the proposed method gains about 2 dB over the performance bound of optimum compressive sensing, about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6 to 7 dB to LASSO-based recovery schemes while enjoying polynomial time complexity. We utilize that in the presence of Gaussian noise the mean-squared error conditioned on the current observation is proportional to the derivative of the conditional mean estimate with respect to this observation.

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arXiv2017

Sparse Bayesian Learning for DOA Estimation in Heteroscedastic Noise

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring stochastic maximum likelihood DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. This SBL approach is more flexible and performs better than high-resolution methods since they cannot estimate the heteroscedastic noise process. An alternative to SBL is simple data normalization, whereby only the phase across the array is utilized. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

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arXiv2016

Multi Snapshot Sparse Bayesian Learning for DOA Estimation

The directions of arrival (DOA) of plane waves are estimated from multi-snapshot sensor array data using Sparse Bayesian Learning (SBL). The prior source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters the unknown variances (i.e. the source powers). For a complex Gaussian likelihood with hyperparameter the unknown noise variance, the corresponding Gaussian posterior distribution is derived. For a given number of DOAs, the hyperparameters are automatically selected by maximizing the evidence and promote sparse DOA estimates. The SBL scheme for DOA estimation is discussed and evaluated competitively against LASSO ($\ell_1$-regularization), conventional beamforming, and MUSIC

math.ST↗

arXiv2015

Using the LASSO's Dual for Regularization in Sparse Signal Reconstruction from Array Data

Waves from a sparse set of source hidden in additive noise are observed by a sensor array. We treat the estimation of the sparse set of sources as a generalized complex-valued LASSO problem. The corresponding dual problem is formulated and it is shown that the dual solution is useful for selecting the regularization parameter of the LASSO when the number of sources is given. The solution path of the complex-valued LASSO is analyzed. For a given number of sources, the corresponding regularization parameter is determined by an order-recursive algorithm and two iterative algorithms that are based on a further approximation. Using this regularization parameter, the DOAs of all sources are estimated.

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arXiv2015

Multiple and single snapshot compressive beamforming

For a sound field observed on a sensor array, compressive sensing (CS) reconstructs the direction-of-arrival (DOA) of multiple sources using a sparsity constraint. The DOA estimation is posed as an underdetermined problem by expressing the acoustic pressure at each sensor as a phase-lagged superposition of source amplitudes at all hypothetical DOAs. Regularizing with an $\ell_1$-norm constraint renders the problem solvable with convex optimization, and promoting sparsity gives high-resolution DOA maps. Here, the sparse source distribution is derived using maximum a posteriori (MAP) estimates for both single and multiple snapshots. CS does not require inversion of the data covariance matrix and thus works well even for a single snapshot where it gives higher resolution than conventional beamforming. For multiple snapshots, CS outperforms conventional high-resolution methods, even with coherent arrivals and at low signal-to-noise ratio. The superior resolution of CS is demonstrated with vertical array data from the SWellEx96 experiment for coherent multi-paths.

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arXiv2014

Time- and Frequency-Varying $K$-Factor of Non-Stationary Vehicular Channels for Safety Relevant Scenarios

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$-factor. We analyze the time-frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS). This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Ricean distributed with a varying $K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bi-modal Gaussian mixture model, that allows to capture the $K$-factor variability in time for safety-relevant ITS scenarios.

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