arXiv subjects

Gerhard Wunder

Publications and source records attributed to Gerhard Wunder.

At least 55 records · Page 3Linked to original sources

arXiv2021

Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding

The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free techniques that work without knowledge about the underlying channel. Such techniques utilize for example generative adversarial networks to estimate and model the conditional channel distribution, mutual information estimation as a reward function, or reinforcement learning. In this paper, the approach of reinforcement learning is studied and, in particular, the policy gradient method for a model-free approach of neural network-based secure encoding is investigated. Previously developed techniques for enforcing a certain co-set structure on the encoding process can be combined with recent reinforcement learning approaches. This new approach is evaluated by extensive simulations, and it is demonstrated that the resulting decoding performance of an eavesdropper is capped at a certain error level.

cs.IT↗

arXiv2021

Measure Concentration on the OFDM-based Random Access Channel

It is well known that CS can boost massive random access protocols. Usually, the protocols operate in some overloaded regime where the sparsity can be exploited. In this paper, we consider a different approach by taking an orthogonal FFT base, subdivide its image into appropriate sub-channels and let each subchannel take only a fraction of the load. To show that this approach can actually achieve the full capacity we provide i) new concentration inequalities, and ii) devise a sparsity capture effect, i.e where the sub-division can be driven such that the activity in each each sub-channel is sparse by design. We show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase.

cs.IT↗

arXiv2021

Hierarchical sparse recovery from hierarchically structured measurements with application to massive random access

A new family of operators, coined hierarchical measurement operators, is introduced and discussed within the well-known hierarchical sparse recovery framework. Such operator is a composition of block and mixing operations and notably contains the Kronecker product as a special case. Results on their hierarchical restricted isometry property (HiRIP) are derived, generalizing prior work on recovery of hierarchically sparse signals from Kronecker-structured linear measurements. Specifically, these results show that, very surprisingly, sparsity properties of the block and mixing part can be traded against each other. The measurement structure is well-motivated by a massive random access channel design in communication engineering. Numerical evaluation of user detection rates demonstrate the huge benefit of the theoretical framework.

cs.IT↗

arXiv2021

ComPass: Proximity Aware Common Passphrase Agreement Protocol for Wi-Fi devices Using Physical Layer Security

Secure and scalable device provisioning is a notorious challenge in Wi-Fi. WPA2/WPA3 solutions take user interaction and a strong passphrase for granted. However, the often weak passphrases are subject to guessing attacks. Notably, there has been a significant rise of cyberattacks on Wi-Fi home or small office networks during the COVID-19 pandemic. This paper addresses the device provisioning problem in Wi-Fi (personal mode) and proposes ComPass protocol to supplement WPA2/WPA3. ComPass replaces the pre-installed or user-selected passphrases with automatically generated ones. For this, ComPass employs Physical Layer Security and extracts credentials from common random physical layer parameters between devices. Two major features make ComPass unique and superior compared to previous proposals: First, it employs phase information (rather than amplitude or signal strength) to generate the passphrase so that it is robust, scaleable, and impossible to guess. Our analysis showed that ComPass generated passphrases have 3 times more entropy than human generated passphrases (113-bits vs. 34-bits). Second, ComPass selects parameters such that two devices bind only within a certain proximity (less than 3m), hence providing practically useful in-build PLS-based authentiation. ComPass is available as a kernel module or as full firmware.

cs.CR↗

arXiv2021

Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

In order to enhance the performance of cyber-physical systems, this paper proposes the integrated de-sign of distributed controllers for distributed plants andthe control of the communication network. Conventionaldesign methods use static interfaces between both enti-ties and therefore rely on worst-case estimations of com-munication delay, often leading to conservative behaviorof the overall system. By contrast, the present approachestablishes a robust distributed model-predictive controlscheme, in which the local subsystem controllers oper-ate under the assumption of a variable communicationschedule that is predicted by a network controller. Us-ing appropriate models for the communication network,the network controller applies a predictive network policyfor scheduling the communication among the subsystemcontrollers across the network. Given the resulting time-varying predictions of the age of information, the papershows under which conditions the subsystem controllerscan robustly stabilize the distributed system. To illustratethe approach, the paper also reports on the application to avehicle platooning scenario.

eess.SY↗

arXiv2020

WiFi-Based Channel Impulse Response Estimation and Localization via Multi-Band Splicing

Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study the problem of channel impulse response (CIR) estimation from commodity WiFi channel state information (CSI). The accuracy of a CIR estimation method in this setup is limited by both the available channel bandwidth as well as various CSI distortions induced by the underlying hardware. We propose a multi-band splicing method that increases channel bandwidth by combining CSI data across multiple frequency bands. In order to compensate for the CSI distortions, we develop a per-band processing algorithm that is able to estimate the distortion parameters and remove them to yield the "clean" CSI. This algorithm incorporates the atomic norm denoising sparse recovery method to exploit channel sparsity. Splicing clean CSI over M frequency bands, we use orthogonal matching pursuit (OMP) as an estimation method to recover the sparse CIR with high (M-fold) resolution. Unlike previous works in the literature, our method does not appeal to any limiting assumption on the CIR (other than the widely accepted sparsity assumption) or any ad hoc processing for distortion removal. We show, empirically, that the proposed method outperforms the state of the art in terms of localization accuracy.

cs.IT↗

arXiv2020

Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison

Deep learning based physical layer design, i.e., using dense neural networks as encoders and decoders, has received considerable interest recently. However, while such an approach is naturally training data-driven, actions of the wireless channel are mimicked using standard channel models, which only partially reflect the physical ground truth. Very recently, neural network based mutual information (MI) estimators have been proposed that directly extract channel actions from the input-output measurements and feed these outputs into the channel encoder. This is a promising direction as such a new design paradigm is fully adaptive and training data-based. This paper implements further recent improvements of such MI estimators, analyzes theoretically their suitability for the channel coding problem, and compares their performance. To this end, a new MI estimator using a \emph{``reverse Jensen''} approach is proposed.

cs.IT↗

arXiv2020

Stability Results on Synchronized Queues in Discrete-Time for Arbitrary Dimension

In a batch of synchronized queues, customers can only be serviced all at once or not at all, implying that service remains idle if at least one queue is empty. We propose that a batch of $n$ synchronized queues in a discrete-time setting is quasi-stable for $n \in \{2,3\}$ and unstable for $n \geq 4$. A correspondence between such systems and a random-walk-like discrete-time Markov chain (DTMC), which operates on a quotient space of the original state-space, is derived. Using this relation, we prove the proposition by showing that the DTMC is transient for $n \geq 4$ and null-recurrent (hence quasi-stability) for $n \in \{2,3\}$ via evaluating infinite power sums over skewed binomial coefficients. Ignoring the special structure of the quotient space, the proposition can be interpreted as a result of Pólya's theorem on random walks, since the dimension of said space is $d-1$.

math.PR↗

arXiv2020

Reliable recovery of hierarchically sparse signals for Gaussian and Kronecker product measurements

We propose and analyze a solution to the problem of recovering a block sparse signal with sparse blocks from linear measurements. Such problems naturally emerge inter alia in the context of mobile communication, in order to meet the scalability and low complexity requirements of massive antenna systems and massive machine-type communication. We introduce a new variant of the Hard Thresholding Pursuit (HTP) algorithm referred to as HiHTP. We provide both a proof of convergence and a recovery guarantee for noisy Gaussian measurements that exhibit an improved asymptotic scaling in terms of the sampling complexity in comparison with the usual HTP algorithm. Furthermore, hierarchically sparse signals and Kronecker product structured measurements naturally arise together in a variety of applications. We establish the efficient reconstruction of hierarchically sparse signals from Kronecker product measurements using the HiHTP algorithm. Additionally, we provide analytical results that connect our recovery conditions to generalized coherence measures. Again, our recovery results exhibit substantial improvement in the asymptotic sampling complexity scaling over the standard setting. Finally, we validate in numerical experiments that for hierarchically sparse signals, HiHTP performs significantly better compared to HTP.

cs.IT↗

arXiv2020

Model-Predictive Control for Discrete-Time Queueing Networks with Varying Topology

In this paper, we equip the conventional discrete-time queueing network with a Markovian input process, that, in addition to the usual short-term stochastics, governs the mid- to long-term behavior of the links between the network nodes. This is reminiscent of so-called Jump-Markov systems in control theory and allows the network topology to change over time. We argue that the common back-pressure control policy is inadequate to control such network dynamics and propose a novel control policy inspired by the paradigms of model-predictive control. Specifically, by defining a suitable but arbitrary prediction horizon, our policy takes into account the future network states and possible control actions. This stands in clear contrast to most other policies which are myopic, i.e. only consider the next state. We show numerically that such an approach can significantly improve the control performance and introduce several variants, thereby trading off performance versus computational complexity. In addition, we prove so-called throughput optimality of our policy which guarantees stability for all network flows that can be maintained by the network. Interestingly, in contrast to general stability proofs in model-predictive control, our proof does not require the assumption of a terminal set (i.e. for the prediction horizon to be large enough). Finally, we provide several illustrating examples, one of which being a network of synchronized queues. This one in particular constitutes an interesting system class, in which our policy exerts superiority over general back-pressure policies, that even lose their throughput optimality in those networks.

eess.SY↗

arXiv2020

The Method of Conditional Expectations for PAPR and Cubic Metric Reduction

The OFDM waveform exhibits high fluctuation in the signal envelope which causes distortion in the nonlinear power amplifier of the transmitter. Peak-to-Average Power Ratio (PAPR) and Cubic Metric (CM) are the common metrics to quantify the phenomenon. A promising approach for PAPR or CM reduction is Sign Selection which is based on altering the signs of the data symbols. In this paper, the Method of Conditional Expectations (CE Method) is proposed to obtain a competing suboptimal solution to the Sign Selection problem. For PAPR reduction, a surrogate metric is introduced which allows for an efficient application of the CE Method. For CM reduction, the tractability of the definition of CM is exploited to this end. The algorithm is analyzed to obtain an upper bound on the worst-case reduced metric value. A noticeable characteristic is the persistent reduction capability for a wide range of subcarrier numbers. In particular, simulations show a reduction of the so-called "effective PAPR" to about 6.5 dB from 10.5 dB and 11.7 dB respectively for 64 to 1024 subcarriers. A similar steady reduction of 3 dB is observed for CM. In addition, the CE Method leads to a pruned version of Sign Selection which halves the rate loss.

cs.IT↗

arXiv2019

Predictive Network Control in Multi-Connectivity Mobility for URLLC Services

This paper proposes a centralized predictive flow controller to handle multi-connectivity for ultra-reliable low latency communication (URLLC) services. The prediction is based on channel state information (CSI) and buffer state reports from the system nodes. For this, we extend CSI availability to a packet data convergence protocol (PDCP) controller. The controller captures CSI in a discrete time Markov chain (DTMC). The DTMC is used to predict forwarding decisions over a finite time horizon. The novel mathematical model optimizes over finite trajectories based on a linear program. The results show performance improvements in a multi-layer small cell mobility scenario in terms of end-to-end (E2E) throughput. Furthermore, 5G new radio (NR) complaint system level simulations (SLS) and results are shown for dual connectivity as well as for the general multi-connectivity case.

cs.IT↗

arXiv2019

Non-Coherent Joint Transmission in Poisson Cellular Networks Under Pilot Contamination

This paper investigates the performance of downlink cellular networks with non-coherent joint (mutlipoint) transmissions and practical channel estimation. Under a stochastic geometry framework, the spatial average signal-to-noise-ratio (SNR) is characterized, taking into account the effect of channel estimation error due to pilot contamination. A simple, easy to compute SNR expression is obtained under the assumption of randomly generated pilot sequences and minimal prior information about the channels and positions of access points (APs). This SNR expression allows for the efficient joint optimization of critical system design parameters such as number of cooperating APs and training overhead. Among others, it is shown that multipoint transmissions are preferable to conventional (non-cooperative) cellular operation under certain operational conditions. Furthermore, analytical insights are obtained regarding (a) the minimum training overhead required to achieve a given SNR degradation compared to the perfect channel estimation case and (b) the optimal number of cooperating APs when an arbitrarily large training overhead can be afforded. For the latter, in particular, a phase transition phenomenon is identified, where the optimal number of cooperating APs is either finite or infinite, depending on whether the path loss factor is less or equal than a certain value, respectively.

cs.IT↗

arXiv2019

The Method of Conditional Expectations for Cubic Metric Reduction in OFDM

High variations in the OFDM signal envelope cause nonlinear distortion in the power amplifier of the transmitter, which is a major drawback. Peak-to-Average Power Ratio (PAPR) and Cubic Metric (CM) are commonly used for quantifying this characteristic of the signal. Despite the reportedly higher accuracy compared to PAPR, limited research has been done on reduction algorithms for CM. In this paper, the Method of Conditional Expectations (CE Method) is used to achieve CM reduction by the Sign Selection approach. Using the CE Method, the amenable mathematical structure of CM is exploited to develop a low complexity algorithm. In addition, guaranteed reduction is analytically proved for every combination of the data symbols. Simulations show a reduction gain of almost 3 dB in Raw Cubic Metric (RCM) for practically all subcarrier numbers, which is achieved using only half the full rate loss of the Sign Selection approach.

cs.IT↗

arXiv2019

Deep Learning for the Gaussian Wiretap Channel

End-to-end learning of communication systems with neural networks and particularly autoencoders is an emerging research direction which gained popularity in the last year. In this approach, neural networks learn to simultaneously optimize encoding and decoding functions to establish reliable message transmission. In this paper, this line of thinking is extended to communication scenarios in which an eavesdropper must further be kept ignorant about the communication. The secrecy of the transmission is achieved by utilizing a modified secure loss function based on cross-entropy which can be implemented with state-of-the-art machine-learning libraries. This secure loss function approach is applied in a Gaussian wiretap channel setup, for which it is shown that the neural network learns a trade-off between reliable communication and information secrecy by clustering learned constellations. As a result, an eavesdropper with higher noise cannot distinguish between the symbols anymore.

cs.IT↗

arXiv2019

Deep Learning for Channel Coding via Neural Mutual Information Estimation

End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating the channel from samples, or rely on reinforcement learning to circumvent this problem. We present a novel approach which utilizes a recently proposed neural estimator of mutual information. We use this estimator to optimize the encoder for a maximized mutual information, only relying on channel samples. Moreover, we show that our approach achieves the same performance as state-of-the-art end-to-end learning with perfect channel model knowledge.

cs.IT↗

arXiv2019

WiFi-Based Indoor Localization via Multi-Band Splicing and Phase Retrieval

We study the problem of indoor localization using commodity WiFi channel state information (CSI) measurements. The accuracy of methods developed to address this problem is limited by the overall bandwidth used by the WiFi device as well as various types of signal distortions imposed by the underlying hardware. In this paper, we propose a localization method that performs channel impulse response (CIR) estimation by splicing measured CSI over multiple WiFi bands. In order to overcome hardware-induced phase distortions, we propose a phase retrieval (PR) scheme that only uses CSI magnitude values to estimate the CIR. To achieve high localization accuracy, the PR scheme involves a sparse recovery step, which exploits the fact that the CIR is sparse over the delay domain, due to the small number of contributing signal paths in an indoor environment. Simulation results indicate that our approach outperforms the state of the art by an order of magnitude (cm-level localization accuracy) for more than 90% of the trials and for various SNR regimes.

cs.NI↗

arXiv2018

Low-Overhead Hierarchically-Sparse Channel Estimation for Multiuser Wideband Massive MIMO

The problem of excessive pilot overhead required for uplink massive MIMO channel estimation is well known, let alone when it is considered along with wideband (OFDM) transmissions. Towards channel estimators that are both efficient and require low-training overhead, compressive sensing (CS) approaches have been increasingly popular, exploiting the sparse nature of the physical channel. However, no analytical insights regarding the overhead required for reliable channel estimation in wideband massive MIMO are available. By observing that the wideband massive MIMO channel can be represented by a vector that is not simply sparse but has well defined structural properties, referred to as hierarchical sparsity, we propose low complexity channel estimators for the multiuser scenario that take this property into account. By employing the framework of the hierarchical restricted isometry property, rigorous performance guarantees for these algorithms are provided suggesting concrete design goals for the user pilot sequences. For a specific design, we analytically characterize the scaling of the required pilot overhead with increasing number of antennas and bandwidth, revealing that, as long as the number of antennas is sufficiently large, it is independent of the per user channel sparsity level as well as the number of active users. Hence, surprisingly, in contrast to the classical setting, pilot overhead can be shifted into spatial dimensions not affecting crucial bandwidth constraints thereby increasing the overall system capacity. These analytical insights are verified by simulation results demonstrating also the superiority of the proposed algorithm over conventional CS algorithms that ignore the hierarchical sparsity property.

cs.IT↗