Andreas Winkelbauer

Vehicular Link Performance: From Real-World Experiments to Reliability Models and Performance Analysis

The enormous potential of cooperative vehicular systems to improve traffic safety and efficiency by wirelessly exchanging information among vehicles, as well as between vehicles and infrastructure, has been a research focus since the early 1990s. Significant evolution of this technology has prompted standardization bodies and automotive industry stakeholders worldwide to pay particular attention to its deployment and development, for which a deep understanding of the influence of every component and parameter is required.

Rate-information-optimal Gaussian channel output compression

We study the maximum rate achievable over a Gaussian channel with Gaussian input under channel output compression. This problem is relevant to receive signal quantization in practical communication systems. We use the Gaussian information bottleneck to provide closed-form expressions for the information-rate function and the rate-information function, which quantify the optimal trade-off between the compression rate and the corresponding end-to-end mutual information. We furthermore show that mean-square error optimal compression of the channel output achieves the optimal trade-off, thereby greatly facilitating the design of channel output quantizers.

Performance evaluation of IEEE 802.11p infrastructure-to-vehicle tunnel measurements

In this contribution, we discuss and analyze results from real-world performance measurements for IEEE 802.11p during September 2010 along the highway S1 near Vienna, Austria. More specifically, we evaluate the frame success ratio and goodput of the IEEE 802.11p physical (PHY) layer for a infrastructure-to-vehicle (I2V) scenario in a tunnel. We report and discuss the observed frame success ratios and goodputs for various PHY parameter settings and investigate the impact of the propagation environment and the traffic situation inside the tunnel.

Joint network-channel coding in the multiple-access relay channel: Beyond two sources

The combination of log-likelihood ratio (LLR) quantization and network coding was previously shown to be a promising compress-and-forward strategy for the multiple access relay channel with two sources. In this paper, we generalize this approach to the case of more than two sources. Our proposed relay scheme consists of a scalar LLR quantizer for each source followed by a network coding (NC) step that suitably combines the quantizer outputs. We use the information bottleneck method to design the quantizers and the NC function. At the destination, an iterative message-passing decoder is used to jointly decode all source messages. Numerical simulations demonstrate the effectiveness of the proposed transmission strategy and its suitability for asymmetric source-relay channel conditions.

The rate-information trade-off for Gaussian vector channels

We consider lossy compression of the output of a Gaussian vector channel. This is relevant for quantizer design in rate-limited feedback and in receiver front-ends. In particular, we study the trade-off between compression rate and mutual information between channel input and compressed channel output. Using the Gaussian information bottleneck we provide closed-form expressions for the information-rate function and the rate-information function. We prove that optimal compression in the rate distortion sense with squared-error distortion does not achieve the optimal rate-information trade-off. The suboptimality of the rate distortion approach is quantified and we give an upper bound on the gap to the optimal rate-information tradeoff. Finally, our results are corroborated by numerical examples.

On the relation between the Gaussian information bottleneck and MSE-optimal rate-distortion quantization

We use the Gaussian information bottleneck (GIB) to investigate the optimal rate-information trade-off for signal compression in linear Gaussian models and we provide a novel interpretation of the GIB in terms of the eigendecomposition of the Wiener filter. We further study mean-square-error-optimal rate-distortion compression preceded by a linear filter. Choosing this filter as square root of the Wiener filter is shown to be rate-information optimal. Finally, we extend our results to jointly stationary Gaussian random processes.

Joint network-channel coding for the asymmetric multiple-access relay channel

In this paper we consider the time-division multiple-access relay channel (MARC). We propose a low-complexity compress-and-forward-based transmission scheme that consists of a scalar quantization of log-likelihood ratios (LLRs), followed by a suitably defined network code. This scheme is well suited also for asymmetric source-relay channel conditions. We use the information bottleneck method (IBM) for designing the LLR quantizers and we propose a modified IBM algorithm with more favorable numerical properties. Finally, we assess the performance of the proposed scheme in AWGN and fading channels using numerical simulations, including a comparison with existing transmission schemes.

Compress-and-forward in the multiple-access relay channel: With or without network coding?

We consider compress-and-forward-based transmission strategies for the multiple-access relay channel. In particular, we study the impact of the capacity on the relay-destination link on the usefulness of network coding in this context. To this end, we compare a transmission scheme with network coding to a simple forwarding scheme without network coding. For both schemes we design optimal log-likelihood ratio (LLR) vector quantizers using the information bottleneck method. Moreover, we provide closed-form expressions for the LLR statistics at the relay which are required for the vector quantizer design. Numerical simulation results show that the usefulness of network coding depends strongly on the capacity of the relay-destination channel as well as on the number of sources.

On efficient soft-input soft-output encoding of convolutional codes

We study efficient algorithms for soft-input soft-output (SISO) encoding of convolutional codes. While the BCJR algorithm has been suggested for SISO encoding, we show that a forward recursion on the codes trellis is sufficient to compute the a posteriori probabilities of the code bits. We further propose a shift-register based SISO encoding algorithm for non-recursive convolutional encoders, whose complexity scales linearly with constraint length and block length. Finally, we assess the complexity of the proposed algorithms and we discuss approximations to facilitate practical implementation.

A model for vehicle-to-infrastructure communications in urban environments

Dependable vehicle-to-infrastructure (V2I) communication links for intelligent transportation systems are aimed at improving traffic safety and efficiency. The future integration of such cooperative systems, in particular in urban environments with challenging propagation conditions, requires adequate deployment of roadside units (RSU). To analyze the joint impact of typical urban characteristics and RSU deployment conditions on the quality of IEEE 802.11p V2I communications, an extensive measurement campaign was performed in Bologna as part of the European FP7 project iTETRIS. Based on the measured data, we identify specific street layouts and propagation impairments, and we estimate the parameters of a previously introduced range-dependent modified Gilbert model. The resulting set of model parameters enables the generation of realistic packet error patterns for any comparable urban environment.