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Massive Uncoordinated Multiple Access for 5G, IoT, Cyber-Physical Systems. Connections between coding theory and sparse recovery. Coding for fiber optic communications. Coding for Data Storage. Spatially-Coupled Codes and their Applications. Low latency communication, short block length codes. Physical Layer Network Coding, Compute-and-Forward, Lattice Coding for Wireless Networks. Joint Source-Channel Coding for Wireless Networks. Soft Decoding of Reed-Solomon Codes. Can Iterative Decoding be Universal?

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Projects - Research Page | commtheory.wikidot.com Reviews

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Massive Uncoordinated Multiple Access for 5G, IoT, Cyber-Physical Systems. Connections between coding theory and sparse recovery. Coding for fiber optic communications. Coding for Data Storage. Spatially-Coupled Codes and their Applications. Low latency communication, short block length codes. Physical Layer Network Coding, Compute-and-Forward, Lattice Coding for Wireless Networks. Joint Source-Channel Coding for Wireless Networks. Soft Decoding of Reed-Solomon Codes. Can Iterative Decoding be Universal?

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2 krishna narayanan's page

3 research projects

4 publications

5 presentations

6 alumni

7 create account

8 projects

9 current research projects

10 recent past projects

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Projects - Research Page | commtheory.wikidot.com Reviews

https://commtheory.wikidot.com

Massive Uncoordinated Multiple Access for 5G, IoT, Cyber-Physical Systems. Connections between coding theory and sparse recovery. Coding for fiber optic communications. Coding for Data Storage. Spatially-Coupled Codes and their Applications. Low latency communication, short block length codes. Physical Layer Network Coding, Compute-and-Forward, Lattice Coding for Wireless Networks. Joint Source-Channel Coding for Wireless Networks. Soft Decoding of Reed-Solomon Codes. Can Iterative Decoding be Universal?

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commtheory.wikidot.com

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Physical Layer Network Coding, Compute-and-Forward, Lattice Codes - Research Page

http://commtheory.wikidot.com/lattice-coding

Physical Layer Network Coding, Compute-and-Forward, Lattice Codes. At the heart of this strategy lies codes constructed from lattices (or nested lattice codes of Erez and Zamir to be specific). We have been interested in seeking answers to many questions within this framework. The questions include - how can we construct lattice codes and decoders in a computationally efficient way to implement the compute-and-forward paradigm? What happens in the presence of asynchronous transmission? And $ pi D$. P-C W...

2

Alumni - Research Page

http://commtheory.wikidot.com/alumni

Jing Li, Associate Professor, Lehigh University. Dung Ngoc Doan, Senior Engineer, Qualcomm Corportate Research and Development, La Jolla, CA. Vivek Gulati, Broadcom Corp, Bay area, CA. Hari Sankar, Qualcomm, La Jolla, CA. Nitin Nangare, Marvell Semiconductors, Santa Clara, CA. Janath Peiris, Qualcomm, La Jolla, CA. Ching Fu Lan, current position unknown. Kapil Bhattad, Senior Engineer, Qualcomm Corporate Research and Development, La Jolla, CA. Brett Hern, Chevron Inc, Houston, TX. Deepak Gilra, Intel, OR.

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Universal Codes - Research Page

http://commtheory.wikidot.com/universal-codes

Universality w.r.t. channel types. Different BMS channels with the same capacity. A single code that performs well over these channels. Universality w.r.t. channel parameters. Rateless codes perform well on one channel type with different capacities. LT codes are universal for the single user scenario (for the erasure channel). Multi-user scenarios not studied. Universality w.r.t. the source correlation. Encoder/Decoder do NOT depend on source statistics. Coding performance is asymptotically the same.

4

A Rate-Distortion Perspective on Multiple Decoding Attempts for Reed-Solomon Codes - Research Page

http://commtheory.wikidot.com/rsdecoding

A Rate-Distortion Perspective on Multiple Decoding Attempts for Reed-Solomon Codes. RS) codes are one of the most widely used error correcting codes. In digital communication and data storage systems. This is primarily due to the fact that RS codes are maximum distance separable (MDS) codes. Can correct long bursts of errors. And have efficient hard-decision decoding (HDD) algorithms, such as the Berlekamp-Massey (BM) algorithm. Which can correct up to half the minimum distance. RS codes of length $n$.

5

Projects - Research Page

http://commtheory.wikidot.com/start

Massive Uncoordinated Multiple Access for 5G, IoT, Cyber-Physical Systems. Connections between coding theory and sparse recovery. Coding for fiber optic communications. Coding for Data Storage. Spatially-Coupled Codes and their Applications. Low latency communication, short block length codes. Physical Layer Network Coding, Compute-and-Forward, Lattice Coding for Wireless Networks. Joint Source-Channel Coding for Wireless Networks. Soft Decoding of Reed-Solomon Codes. Can Iterative Decoding be Universal?

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krishnanarayanan.wikidot.com

Side - Krishna Narayanan

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Eric D. Rubin '06 Professor in Electrical and Computer Engineering, Texas A&M University. Page revision: 63, last edited: 20 Jan 2016 21:20. Powered by Wikidot.com. Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License. Click here to edit contents of this page. Click here to toggle editing of individual sections of the page (if possible). Watch headings for an edit link when available. Append content without editing the whole page source.

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Krishna Narayanan

http://krishnanarayanan.wikidot.com/start

Eric D. Rubin '06 Professor in Electrical and Computer Engineering, Texas A&M University. Aug 2016 - I have been appointed as Eric D. Rubin '06 professor in Electrical and Computer Engineering at Texas A&M. Aug 2016 - Our proposal on Advanced Coding Techniques for Optical Communications has been recommended for funding by NSF. July 2016 - My paper with Avinash Vem and Nagaraj Thenkarai Janakiraman has been accepted to be presented at the Information Theory Workshop in Cambridge, UK in September 2016.

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Massive Uncoordinated Multiple Access for 5G, IoT, Cyber-Physical Systems. Connections between coding theory and sparse recovery. Coding for fiber optic communications. Coding for Data Storage. Spatially-Coupled Codes and their Applications. Low latency communication, short block length codes. Physical Layer Network Coding, Compute-and-Forward, Lattice Coding for Wireless Networks. Joint Source-Channel Coding for Wireless Networks. Soft Decoding of Reed-Solomon Codes. Can Iterative Decoding be Universal?

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