Andreas Winkelbauer

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In this paper we consider the time-division multipleaccess 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(More)
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(More)
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(More)
We consider network coding for the time-division two-way relay channel (TWRC). Based on achievable rates, we study the optimal transmission time allocation for a three-phase transmission protocol. While existing network coding schemes for the TWRC perform "hard" network coding, we propose a novel soft-information-based joint network-channel coding scheme. A(More)
We consider the problem of channel-optimized vector quantization (COVQ) with mutual information as fidelity criterion. This problem is relevant in a communications context, where the goal is to maximize the end-to-end rate. We propose an algorithm which is similar to the information bottleneck method and solves the considered COVQ problem. In contrast to(More)
The calculation of exact outage probabilities in delayconstrained multiuser-systems has been an unsolved problem. This paper introduces an analytical method to calculate the probability of an outage for max-based schedulers, which take a scheduling decision by choosing the user with an associated maximum metric. While this analysis is, therefore, suited for(More)
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 code's trellis is sufficient to compute the a posteriori probabilities of the code bits. We further propose a shift-register based SISO encoding algorithm for(More)
Aiming at improvements in traffic safety and efficiency, dependable infrastructure-to-vehicle (V2I) communication links for intelligent transportation systems require an adequate deployment of roadside units (RSU). However, evaluating the dependence of key system performance indicators on RSU deployment conditions through field tests is expensive,(More)