Modern Coding Theory

By T. Richardson, Rüdiger L. Urbanke. 2008

2,183 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7714eed8311ebaab18f8cbf96d850dc093fb5338

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems (2011, methodology): “Deviating slightly from regular degrees, we propose using a simple random construction known as conﬁguration model in random graph literature [RU08, Bol01].”
The Noise-Sensitivity Phase Transition in Compressed Sensing (2010, background): “ passing algorithms, and most notably belief propagation, have been intensively investigated in coding theory and communications, in particular because of their success in decoding sparse graph codes [RU08]. Belief propagation is dened whenever the a posteriori joint distribution of the variables to”
Continuous‐Variable Quantum Key Distribution with Gaussian Modulation—The Theory of Practical Implementations (2017, methodology): “ reconciliation schemes have been proposed. Two important schemes are slice reconciliation [61] and multidimensional reconciliation [62]. Both schemes can employ low-density parity-check (LDPC) codes [63] for the actual error correction. In the reverse reconciliation scheme one or several LDPC codes”
Staircase Codes: FEC for 100 Gb/s OTN (2012, background): “Staircase codes have a simple graphical representation, which provides a multiedge-type [3] interpretation of their construction.”
Spatially coupled ensembles universally achieve capacity under belief propagation (2012, methodology): “For the BEC this coincides with the EXIT functional (see [18]).”
Quantum error correction (2001, neutral): “See the textbook by Richardson and Urbanke for details [RU08].”
How to Construct Polar Codes (2011, background): “In this paper, we further extend the ideas of [17,18].”
Graphical Models, Exponential Families, and Variational Inference (2008, background): “One important domain in which hard-core constraints arise is communication theory, and in particular the problem of error-control coding [84, 164, 196].”
NR - The New 5G Radio-Access Technology (2018, background): “Given the very high data rates supported by NR, channelcoding for data transmission is based on low-density paritycheck (LDPC) codes [2].”
Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices (2012, methodology): “This statistical analysis of BP iterations is known as the “cavity method” (in statistical physics) [46, 47], the “density evolution” in coding [48], and the “state evolution” in the context of CS [7, 15].”
Message-passing algorithms for compressed sensing (2009, methodology): “We now consider a modiﬁcation of iterative thresholding inspired by message passing algorithms for inference in graphical models [16], and graph-based error correcting codes [17], [18].”
Polar Codes for Channel and Source Coding (2009, background): “6 (Pe(W ) and Z(W ) [51]).”
Deep Learning Methods for Improved Decoding of Linear Codes (2017, methodology): “Recall that this is a fundamental property of message passing algorithms [21].”
Statistical physics-based reconstruction in compressed sensing (2011, background): “…a message-passing algorithm adapted from belief propagation [15], which is a procedure known to be eﬃcient in various hard computational problems [16, 17], and 3) an innovative design of the measurement matrix inspired from the theory of crystal nucleation in statistical physics and from recent…”
Bit-Interleaved Coded Modulation (2008, methodology): “These modern codes [97] approach the capacity of binary-input channels with low complexity.”
Group testing: an information theory perspective (2019, positive): “, see [159]) can play a role in practical group testing constructions, and complementing the extensive use of information theory for theoretical studies of group testing.”
Message passing algorithms for compressed sensing: I. motivation and construction (2009, background): “tention. Remarkable exceptions include [13], [14], [15]. C. State evolution and replica calculations In the context of coding theory, message passing algorithms are analyzed through density evolution [16]. The common justiﬁcation for density evolution is that the underlying g raph is random and spar”