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Andrew Thangaraj
Researcher at Indian Institute of Technology Madras
Publications - 139
Citations - 1926
Andrew Thangaraj is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Low-density parity-check code & Communication channel. The author has an hindex of 19, co-authored 133 publications receiving 1752 citations. Previous affiliations of Andrew Thangaraj include Indian Institutes of Technology & Georgia Institute of Technology.
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Journal ArticleDOI
Applications of LDPC Codes to the Wiretap Channel
TL;DR: This correspondence provides an alternative view of the proof for secrecy capacity of wire tap channels and shows how capacity achieving codes can be used to achieve the secrecy capacity for any wiretap channel, and shows that it is possible to construct linear-time decodable secrecy codes based on low-density parity-check codes that achieve secrecy.
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Error-Control Coding for Physical-Layer Secrecy
TL;DR: System engineers are provided with explicit tools to build simple secrecy codes in order to stimulate interest and foster their integration in communication system prototypes, and the open challenges and opportunities faced for the integration of these codes in practical systems are highlighted.
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Strong Secrecy on the Binary Erasure Wiretap Channel Using Large-Girth LDPC Codes
TL;DR: It is shown using density evolution analysis that the expected bit-error probability of these ensembles, when passed through a binary erasure channel with erasure probability ϵ, decays as O, which guarantees that the coset coding scheme using the dual sequence provides strong secrecy over thebinary erasure wiretap channel for erasure probabilities greater than 1-ϵ.
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Capacity Bounds for Discrete-Time, Amplitude-Constrained, Additive White Gaussian Noise Channels
TL;DR: In this paper, a dual capacity expression is used to derive analytic capacity upper bounds for scalar and vector AWGN channels with an amplitude constraint, and an analytic lower bound is derived by using a concentric constellation and is shown to be within 1 bit of capacity.
Proceedings ArticleDOI
LDPC-based Gaussian key reconciliation
TL;DR: A new information reconciliation method is proposed which allows two parties sharing continuous random variables to agree on a common bit string and achieves higher efficiency than previously reported results.