arXiv subjects

Peter Händel

Publications and source records attributed to Peter Händel.

At least 19 recordsLinked to original sources

arXiv2020

Impact of Backward Crosstalk in 2x2 MIMO Transmitters on NMSE and Spectral Efficiency

We consider backward crosstalk in 2 x 2 transmitters, which is caused by crosstalk from the outputs of the transmitter to the inputs or by the combination of output crosstalk and impedance mismatch. We analyze its impact via feedback networks together with third-order power amplifier non-linearities. We utilize the Bussgang decomposition to express the distorted output signals of the transmitter as a linear transformation of the input plus uncorrelated distortion. The normalized mean-square errors (NMSEs) between the distorted and desired amplified signals are expressed analytically and the optimal closed-form power back-off that minimizes the worst NMSE of the two branches is derived. In the second part of the paper, an achievable spectral efficiency (SE) is presented for the communication from this ``dirty'' transmitter to another single-antenna receiver. The SE-maximizing precoder is optimally found by exploiting the hardware characteristics. Furthermore, the optimal power back-off is analyzed for two sub-optimal precoders, which either do not exploit any hardware knowledge or only partial knowledge. The simulation results show that the performance of these sub-optimal precoders is close-to-optimal. We also discuss how the analysis in this paper can be extended to transmitters with an arbitrary number of antenna branches.

eess.SP↗

arXiv2020

Clock synchronization over networks -- Identifiability of the sawtooth model

In this paper, we analyze the two-node joint clock synchronization and ranging problem. We focus on the case of nodes that employ time-to-digital converters to determine the range between them precisely. This specific design choice leads to a sawtooth model for the captured signal, which has not been studied before from an estimation theoretic standpoint. In the study of this model, we recover the basic conclusion of a well-known article by Freris, Graham, and Kumar in clock synchronization. More importantly, we discover a surprising identifiability result on the sawtooth signal model: noise improves the theoretical condition of the estimation of the phase and offset parameters. To complete our study, we provide performance references for joint clock synchronization and ranging using the sawtooth signal model by presenting an exhaustive simulation study on basic estimation strategies under different realistic conditions. With our contributions in this paper, we enable further research in the estimation of sawtooth signal models and pave the path towards their industrial use for clock synchronization and ranging.

eess.SY↗

arXiv2020

Clock synchronization over networks using sawtooth models

Clock synchronization and ranging over a wireless network with low communication overhead is a challenging goal with tremendous impact. In this paper, we study the use of time-to-digital converters in wireless sensors, which provides clock synchronization and ranging at negligible communication overhead through a sawtooth signal model for round trip times between two nodes. In particular, we derive Cramér-Rao lower bounds for a linearitzation of the sawtooth signal model, and we thoroughly evaluate simple estimation techniques by simulation, giving clear and concise performance references for this technology.

eess.SP↗

arXiv2019

Predicting Graph Signals using Kernel Regression where the Input Signal is Agnostic to a Graph

We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical quantities. In particular, the input may not be a graph signal at all or it could be agnostic to an underlying graph. We use a training dataset to learn the proposed regression model by formulating it as a convex optimization problem, where we use a graph-Laplacian based regularization to enforce that the predicted target is a graph signal. Once the model is learnt, it can be directly used on a large number of test data points one-by-one independently to predict the corresponding targets. Our approach employs kernels between the various input observations, and as a result the kernels are not restricted to be functions of the graph adjacency/Laplacian matrix. We show that the proposed kernel regression exhibits a smoothing effect, while simultaneously achieving noise-reduction and graph-smoothness. We then extend our method to the case when the underlying graph may not be known apriori, by simultaneously learning an underlying graph and the regression coefficients. Using extensive experiments, we show that our method provides a good prediction performance in adverse conditions, particularly when the training data is limited in size and is noisy. In graph signal reconstruction experiments, our method is shown to provide a good performance even for a highly under-determined subsampling.

cs.IT↗

arXiv2018

Gaussian Processes Over Graphs

We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph. We incorporate this information using a graph- Laplacian based regularization which enforces the target vectors to have a specific profile in terms of graph Fourier transform coeffcients, for example lowpass or bandpass graph signals. We discuss how the regularization affects the mean and the variance in the prediction output. In particular, we prove that the predictive variance of the GPG is strictly smaller than the conventional Gaussian process (GP) for any non-trivial graph. We validate our concepts by application to various real-world graph signals. Our experiments show that the performance of the GPG is superior to GP for small training data sizes and under noisy training.

stat.ML↗

arXiv2018

Extreme Learning Machine for Graph Signal Processing

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.

stat.ML↗

arXiv2018

Multi-kernel Regression For Graph Signal Processing

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We show that the resulting optimization problem is shown to be convex and pro- pose an accelerated projected gradient descent based solution. Simulation results using real-world graph signals show efficiency of the multi-kernel based approach over a standard kernel based approach.

stat.ML↗

arXiv2018

Hilbert Transform, Analytic Signal, and Modulation Analysis for Graph Signal Processing

We propose Hilbert transform (HT) and analytic signal (AS) construction for signals over graphs. This is motivated by the popularity of HT, AS, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of HT and AS use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT). We show that a real graph signal (GS) can be represented using smaller number of GFT coefficients than the signal length. We show that the graph HT (GHT) and graph AS (GAS) operations are linear and shift-invariant over graphs. Using the GAS, we define the amplitude, phase, and frequency modulations for a graph signal (GS). Further, we use convex optimization to develop an alternative definition of envelope for a GS. We illustrate the proposed concepts by showing applications to synthesized and real-world signals. For example, we show that the GHT is suitable for anomaly detection/analysis over networks and that GAS reveals complementary information in speech signals.

cs.IT↗

arXiv2017

CLIMEX: A Wireless Physical Layer Security Protocol Based on Clocked Impulse Exchanges

A novel method and protocol establishing common secrecy based on physical parameters between two users is proposed. The four physical parameters of users are their clock frequencies, their relative clock phases and the distance between them. The protocol proposed between two users is backed by theoretical model for the measurements. Further, estimators are proposed to estimate secret physical parameters. Physically exchanged parameters are shown to be secure by virtue of their non-observability to adversaries. Under a simplified analysis based on a testbed settings, it is shown that 38 bits of common secrecy can be derived for one run of the proposed protocol among users. The method proposed is also robust against various kinds of active timing attacks and active impersonating adversaries.

cs.CR↗

arXiv2017

Schedule based Self Localization of asynchronous wireless nodes with experimental validation

In this paper we have proposed clock error mitigation from the measurements in the scheduled based self localization system. We propose measurement model with clock errors while following a scheduled transmission among anchor nodes. Further, RLS algorithm is proposed to estimate clock error and to calibrate measurements of self localizing node against relative clock errors of anchor nodes. A full-scale experimental validation is provided based on commercial off-the-shelf UWB radios under IEEE-standardized protocols.

cs.NI↗

arXiv2016

Map-aided Dead-reckoning --- A Study on Locational Privacy in Insurance Telematics

We present a particle-based framework for estimating the position of a vehicle using map information and measurements of speed. Two measurement functions are considered. The first is based on the assumption that the lateral force on the vehicle does not exceed critical limits derived from physical constraints. The second is based on the assumption that the driver approaches a target speed derived from the speed limits along the upcoming trajectory. Performance evaluations of the proposed method indicate that end destinations often can be estimated with an accuracy in the order of $100\,[m]$. These results expose the sensitivity and commercial value of data collected in many of today's insurance telematics programs, and thereby have privacy implications for millions of policyholders. We end by discussing the strengths and weaknesses of different methods for anonymization and privacy preservation in telematics programs.

cs.CR↗

arXiv2016

The $β$-model for Random Graphs --- Regression, Cramér-Rao Bounds, and Hypothesis Testing

We develop a maximum-likelihood based method for regression in a setting where the dependent variable is a random graph and covariates are available on a graph-level. The model generalizes the well-known $β$-model for random graphs by replacing the constant model parameters with regression functions. Cramér-Rao bounds are derived for the undirected $β$-model, the directed $β$-model, and the generalized $β$-model. The corresponding maximum likelihood estimators are compared to the bounds by means of simulations. Moreover, examples are given on how to use the presented maximum likelihood estimators to test for directionality and significance. Last, the applicability of the model is demonstrated using dynamic social network data describing communication among healthcare workers.

stat.ME↗

arXiv2016

Smartphone-based Vehicle Telematics - A Ten-Year Anniversary

Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

cs.CY↗

arXiv2016

Optimal Scheduling for Interference Mitigation by Range Information

The multiple access scheduling decides how the channel is shared among the nodes in the network. Typical scheduling algorithms aims at increasing the channel utilization and thereby throughput of the network. This paper describes several algorithms for generating an optimal schedule in terms of channel utilization for multiple access by utilizing range information in a fully connected network. We also provide detailed analysis for the proposed algorithms performance in terms of their complexity, convergence, and effect of non-idealities in the network. The performance of the proposed schemes are compared with non-aided methods to quantify the benefits of using the range information in the communication. The proposed methods have several favorable properties for the scalable systems. We show that the proposed techniques yields better channel utilization and throughput as the number of nodes in the network increases. We provide simulation results in support of this claim. The proposed methods indicate that the throughput can be increased on average by 3-10 times for typical network configurations.

cs.IT↗

arXiv2016

Scalable and Passive Wireless Network Clock Synchronization

Clock synchronization is ubiquitous in wireless systems for communication, sensing and control. In this paper we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions. If such information is available the framework developed here takes position uncertainties into account. In the absence of such information we propose a mechanism which enables simultaneous synchronization and positioning. Furthermore we derive the Cramer-Rao bounds for the system which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method which is statistically efficient.

cs.NI↗

arXiv2016

Inertial Sensor Arrays, Maximum Likelihood, and Cramér-Rao Bound

A maximum likelihood estimator for fusing the measurements in an inertial sensor array is presented. The maximum likelihood estimator is concentrated and an iterative solution method is presented for the resulting low-dimensional optimization problem. The Cramér-Rao bound for the corresponding measurement fusion problem is derived and used to assess the performance of the proposed method, as well as to analyze how the geometry of the array and sensor errors affect the accuracy of the measurement fusion. The angular velocity information gained from the accelerometers in the array is shown to be proportional to the square of the array dimension and to the square of the angular speed. In our simulations the proposed fusion method attains the Cramér-Rao bound and outperforms the current state-of-the-art method for measurement fusion in accelerometer arrays. Further, in contrast to the state-of-the-art method that requires a 3D array to work, the proposed method also works for 2D arrays. The theoretical findings are compared to results from real-world experiments with an in-house developed array that consists of 192 sensing elements.

stat.AP↗

arXiv2016

IR-UWB Detection and Fusion Strategies using Multiple Detector Types

Optimal detection of ultra wideband (UWB) pulses in a UWB transceiver employing multiple detector types is proposed and analyzed in this paper. We propose several fusion techniques for fusing decisions made by individual IR-UWB detectors. We assess the performance of these fusion techniques for commonly used detector types like matched filter, energy detector and amplitude detector. In order to perform this, we derive the detection performance equation for each of the detectors in terms of false alarm rate, shape of the pulse and number of UWB pulses used in the detection and apply these in the fusion algorithms. We show that the performance can be improved approximately by 4 dB in terms of signal to noise ratio (SNR) for perfect detectability of a UWB signal in a practical scenario by fusing the decisions from individual detectors.

cs.IT↗

arXiv2015

Noise robust integration for blind and non-blind reverberation time estimation

The estimation of the decay rate of a signal section is an integral component of both blind and non-blind reverberation time estimation methods. Several decay rate estimators have previously been proposed, based on, e.g., linear regression and maximum-likelihood estimation. Unfortunately, most approaches are sensitive to background noise, and/or are fairly demanding in terms of computational complexity. This paper presents a low complexity decay rate estimator, robust to stationary noise, for reverberation time estimation. Simulations using artificial signals, and experiments with speech in ventilation noise, demonstrate the performance and noise robustness of the proposed method.

cs.SD↗