Publications by Satish Babu Korada

Exchange of Limits: Why Iterative Decoding Works

We consider communication over binary-input memoryless output-symmetric channels using low-density parity-check codes and message-passing decoding. The asymptotic (in the length) performance of such a combination for a fixed number of iterations is given b ...

2011

Exchange of Limits: Why Iterative Decoding Works

Rüdiger Urbanke, Satish Babu Korada

We consider communication over binary-input memoryless output-symmetric channels using low-density parity-check codes and message-passing decoding. The asymptotic (in the length) performance of such a combination for a fixed number of iterations is given b ...

Institute of Electrical and Electronics Engineers2011

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Rüdiger Urbanke, Satish Babu Korada, Eren Sasoglu

Polar codes were recently introduced by Arikan. They achieve the capacity of arbitrary symmetric binary-input discrete memoryless channels under a low complexity successive cancellation decoding strategy. The original polar code construction is closely rel ...

2010

Tight Bounds on the Capacity of Binary Input random CDMA Systems

Nicolas Macris, Satish Babu Korada

We consider multiple access communication on a binary input additive white Gaussian noise channel using randomly spread code division. For a general class of symmetric distributions for spreading coefficients, in the limit of a large number of users, we pr ...

2010

Polar Codes are Optimal for Lossy Source Coding

Rüdiger Urbanke, Satish Babu Korada

We consider lossy source compression of a binary symmetric source using polar codes and the low-complexity successive encoding algorithm. It was recently shown by Arikan that polar codes achieve the capacity of arbitrary symmetric binary-input discrete mem ...

Institute of Electrical and Electronics Engineers2010

Lossless Source Coding with Polar Codes

Satish Babu Korada, Harm Cronie

In this paper lossless compression with polar codes is considered. A polar encoding algorithm is developed and a method to design the code and compute the average compression rate for finite lengths is given. It is shown that the scheme achieves the optima ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Performance of Polar Codes for Channel and Source Coding

Rüdiger Urbanke, Satish Babu Korada, Nadine Hussami

Polar codes, introduced recently by Arikan, are the first family of codes known to achieve capacity of symmetric channels using a low complexity successive cancellation decoder. Although these codes, combined with successive cancellation, are optimal in th ...

2009

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Rüdiger Urbanke, Satish Babu Korada, Eren Sasoglu

Polarization codes were recently introduced by Ar\i kan. They are explicit code constructions which achieve the capacity of arbitrary symmetric binary-input discrete memoryless channels (and even extensions thereof) under a low complexity successive decodi ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Polar codes: Characterization of exponent, bounds, and constructions

Rüdiger Urbanke, Satish Babu Korada, Eren Sasoglu

Polar codes were recently introduced by Arikan. They achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels under a low complexity successive cancellation decoding strategy. The original polar code construction is closely rel ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Polar codes for channel and source coding

Satish Babu Korada

The two central topics of information theory are the compression and the transmission of data. Shannon, in his seminal work, formalized both these problems and determined their fundamental limits. Since then the main goal of coding theory has been to find ...

EPFL2009

Satish Babu Korada

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Exchange of Limits: Why Iterative Decoding Works

Exchange of Limits: Why Iterative Decoding Works

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Tight Bounds on the Capacity of Binary Input random CDMA Systems

Polar Codes are Optimal for Lossy Source Coding

Lossless Source Coding with Polar Codes

Performance of Polar Codes for Channel and Source Coding

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Polar codes: Characterization of exponent, bounds, and constructions

Polar codes for channel and source coding

Exchange of Limits: Why Iterative Decoding Works

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Exchange of Limits: Why Iterative Decoding Works

Tight Bounds on the Capacity of Binary Input random CDMA Systems

Polar Codes are Optimal for Lossy Source Coding

Lossless Source Coding with Polar Codes

Performance of Polar Codes for Channel and Source Coding

Polar Codes: Characterization of Exponent, Bounds, and Constructions

Polar codes: Characterization of exponent, bounds, and constructions

Polar codes for channel and source coding