Error Control Coding
By I. Otung. IEEE Transactions on Information Theory, 2004
1,882 citations recorded by the source corpus.
Error control coding has been an area of intense research for more than 50 years and a vast array of techniques has been developed, which cannot all be covered even in a book devoted exclusively to coding. In this chapter, we primarily focus on imparting a clear understanding of the principles involved in error control coding in general, and on the parameters and design constraints that govern their use in communication system design. We also discuss linear binary block coding and the non-binary Reed-Solomon code, including a few worked examples to fully explain their coding and decoding algorithms.
Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/f9000e8e1e1851a3b95dcc3c3b02cd3b2788ab11
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Dimitris Papailiopoulos
- Pulkit Grover
- Suhas N. Diggavi
- Sanghamitra Dutta
- Viveck R. Cadambe
- Ramtin Pedarsani
- Deepesh Data
- Eleni Diamanti
In Computer Networks and Communications
- Dimitris Papailiopoulos
- Onur Mutlu
- Pulkit Grover
- Dina Katabi
- Sachin Rajsekhar Katti
- Ramtin Pedarsani
- Sanghamitra Dutta
- Muriel Médard
In Electrical and Electronic Engineering
- Dina Katabi
- Sachin Rajsekhar Katti
- Onur Mutlu
- Muriel Médard
- Hari Balakrishnan
- Kannan Ramchandran
- Yu Cai
- Erich F. Haratsch
Citations in the corpus
- A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “el between transmitter and receiver, as well as the definition and solution of a specific optimization problem. The produced solution would yield optimality guarantees under the given model (see, e.g., [8]). In contrast, at a high level, the machine learning approach substitutes the time-consuming ste”
- “Short-Dot”: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products (2017, methodology): “In this context, optimized Repetition and Maximum Distance Separable (MDS) [12] [13] codes have been explored recently [1] [2] [3] to speed up computations.”
- Cyclic redundancy code (CRC) polynomial selection for embedded networks (2004, background): “[Lin83] states that polynomial selection is “a very difficult problem” and says that some good cyclic codes have been discovered, but provides no details.”
- Speeding Up Distributed Machine Learning Using Codes (2015, background): “robustness to noise [79]. It encodes a message of ksymbols into a longer message of ncoded symbols such that the original kmessage symbols can be recovered by decoding a subset of coded symbols [79], [80]. We now show how erasure codes can be applied to distributed computation to mitigate the stragg”
- Speeded up detection of squared fiducial markers (2018, result): “As a consequence, 86 other authors improved that work by using binary BCH 87 codes [30] (which allows a more robust error detection) and 88 named it ARToolKit+ [31].”
- Quantum key distribution over 25 km with an all-fiber continuous-variable system (2007, methodology): “Finally, a few extra parity bits are obtained by applying an algebraic code (such as a BCH code [34]) to the whole data sequence.”
- Data-Hiding Codes (2005, neutral): “This observationwillbeof interest inSectionVIII-B.”
- Pyramid Codes: Flexible Schemes to Trade Space for Access Efficiency in Reliable Data Storage Systems (2007, background): “In the first example, the configuration in Figure 7 is revisited, which turns out to be a simple form of product codes [17].”
- Symbol-level network coding for wireless mesh networks (2008, background): “Similarly, prior proposals forexploiting partially correct receptions, like PPR[8], SOFT[29], and H-ARQ[16], limit themselves toa single wireless hop, incurring signi.cant overhead trying to make that hop reliable.”
- Threshold voltage distribution in MLC NAND flash memory: Characterization, analysis, and modeling (2013, background): “Index Terms NAND Flash, Memory Reliability, Memory Signal Processing, Threshold Voltage Distribution, Read Retry I. INTRODUCTION During the past decade, the capacity of NAND flash memory has increased more than 1000 times as a result of aggressive process scaling and multi-level cell (MLC)…”