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—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)
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)
—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(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)
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(More)
—The calculation of exact outage probabilities in delay-constrained 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(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 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.(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)