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Aleksandar Kavcic
Researcher at University of Hawaii
Publications - 137
Citations - 4750
Aleksandar Kavcic is an academic researcher from University of Hawaii. The author has contributed to research in topics: Intersymbol interference & Communication channel. The author has an hindex of 33, co-authored 132 publications receiving 4534 citations. Previous affiliations of Aleksandar Kavcic include Purdue University & Ruhr University Bochum.
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Simulation-Based Computation of Information Rates for Channels With Memory
TL;DR: The information rate of finite-state source/channel models can be accurately estimated by sampling both a long channel input sequence and the corresponding channel output sequence, followed by a forward sum-product recursion on the joint source/ channel trellis.
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The Feasibility of Magnetic Recording at 10 Terabits Per Square Inch on Conventional Media
TL;DR: The feasibility of a new approach to magnetic recording based on shingled writing and two-dimensional readback and signal-processing and the significant challenges that must be overcome are examined.
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Binary intersymbol interference channels: Gallager codes, density evolution, and code performance bounds
TL;DR: The noise tolerance threshold is computed using a suitably developed density evolution algorithm and verified, by simulation, that the thresholds represent accurate predictions of the performance of the iterative sum-product algorithm for finite (but large) block lengths.
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Equal-diagonal QR decomposition and its application to precoder design for successive-cancellation detection
TL;DR: An optimal QR decomposition is proposed, which is called the equal-diagonal QR decompose, or briefly the QRS decomposition, and the performance of the QR detector is asymptotically equivalent to that of the maximum-likelihood detector (MLD) that uses the same precoder.
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The Viterbi algorithm and Markov noise memory
TL;DR: This work designs sequence detectors for channels with intersymbol interference (ISI) and correlated (and/or signal-dependent) noise and derives the optimal maximum-likelihood sequence detector (MLSD) and the optimalmaximum a posteriori (MAP) sequence detector extending to the correlated noise case the Viterbi algorithm.