Results 1 - 10 of 1,309

Iterative decoding of binary block and convolutional codes

by Joachim Hagenauer, Elke Offer, Lutz Papke - IEEE TRANS. INFORM. THEORY , 1996
"... Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft chann ..."
Abstract - Cited by 610 (43 self) - Add to MetaCart
channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a

The Capacity of Low-Density Parity-Check Codes Under Message-Passing Decoding

by Thomas J. Richardson, Rüdiger L. Urbanke , 2001
"... In this paper, we present a general method for determining the capacity of low-density parity-check (LDPC) codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chos ..."
Abstract - Cited by 574 (9 self) - Add to MetaCart
In this paper, we present a general method for determining the capacity of low-density parity-check (LDPC) codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly

THE ENTROPY RATE OF A BINARY CHANNEL WITH SLOWLY SWITCHING INPUT

by P. Chigansky , 2006
"... Abstract. In this note an asymptotic lower bound is derived for the entropy rate of the output of binary channel, whose input is a slowly switching Markov chain. The proof relies on certain concentration properties of conditional distribution (filtering) process. 1. ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract. In this note an asymptotic lower bound is derived for the entropy rate of the output of binary channel, whose input is a slowly switching Markov chain. The proof relies on certain concentration properties of conditional distribution (filtering) process. 1.

Computation of channel capacity and rate-distortion functions

by Richard E. Blahut - IEEE Trans. Inform. Theory , 1972
"... A&r&-By defining mutual information as a maximum over an appropriate space, channel capacities can be defined as double maxima and rate-distortion functions as double minima. This approach yields valuable new insights regarding the computation of channel capacities and rate-distortion functi ..."
Abstract - Cited by 280 (1 self) - Add to MetaCart
-distortion functions. In particular, it suggests a simple algo-rithm for computing channel capacity that consists of a mapping from the set of channel input probability vectors into itself such that the sequence of probability vectors generated by successive applications of the mapping converges to the vector

On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit

by Sae-young Chung, G. David Forney, Jr., Thomas J. Richardson, Rüdiger Urbanke - IEEE COMMUNICATIONS LETTERS , 2001
"... We develop improved algorithms to construct good low-density parity-check codes that approach the Shannon limit very closely. For rate 1/2, the best code found has a threshold within 0.0045 dB of the Shannon limit of the binary-input additive white Gaussian noise channel. Simulation results with a ..."
Abstract - Cited by 306 (6 self) - Add to MetaCart
We develop improved algorithms to construct good low-density parity-check codes that approach the Shannon limit very closely. For rate 1/2, the best code found has a threshold within 0.0045 dB of the Shannon limit of the binary-input additive white Gaussian noise channel. Simulation results with a

Secure Program Execution via Dynamic Information Flow Tracking

by G. Edward Suh, Jaewook Lee, Srinivas Devadas , 2004
"... Dynamic information flow tracking is a hardware mechanism to protect programs against malicious attacks by identifying spurious information flows and restricting the usage of spurious information. Every security attack to take control of a program needs to transfer the program’s control to malevolen ..."
Abstract - Cited by 271 (3 self) - Add to MetaCart
to malevolent code. In our approach, the operating system identifies a set of input channels as spurious, and the processor tracks all information flows from those inputs. A broad range of attacks are effectively defeated by disallowing the spurious data to be used as instructions or jump target addresses. We

Control architecture in optical burst-switched WDM networks

by Yijun Xiong, Marc Vandenhoute, Hakki C. Cankaya - IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS , 2000
"... Optical burst switching (OBS) is a promising solution for building terabit optical routers and realizing IP over WDM. In this paper, we describe the basic concept of OBS and present a general architecture of optical core routers and electronic edge routers in the OBS network. The key design issues ..."
Abstract - Cited by 201 (1 self) - Add to MetaCart
Optical burst switching (OBS) is a promising solution for building terabit optical routers and realizing IP over WDM. In this paper, we describe the basic concept of OBS and present a general architecture of optical core routers and electronic edge routers in the OBS network. The key design issues

Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation

by Sae-Young Chung, Thomas J. Richardson, Rüdiger L. Urbanke - IEEE TRANS. INFORM. THEORY , 2001
"... Density evolution is an algorithm for computing the capacity of low-density parity-check (LDPC) codes under messagepassing decoding. For memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels and sum-product decoders, we use a Gaussian approximation for message densi ..."
Abstract - Cited by 244 (2 self) - Add to MetaCart
Density evolution is an algorithm for computing the capacity of low-density parity-check (LDPC) codes under messagepassing decoding. For memoryless binary-input continuous-output additive white Gaussian noise (AWGN) channels and sum-product decoders, we use a Gaussian approximation for message

Multipleantenna channel hardening and its implications for rate feedback and scheduling

by Bertrand M. Hochwald, Thomas L. Marzetta, Vahid Tarokh - IEEE Transactions on Information Theory , 2004
"... Wireless data traffic is expected to grow over the next few years and the technologies that will provide data services are still being debated. One possibility is to use multiple antennas at basestations and terminals to get very high spectral efficiencies in rich scattering environments. Such multi ..."
Abstract - Cited by 159 (2 self) - Add to MetaCart
. Such multiple-input multiple-output (MIMO) channels can then be used in conjunction with scheduling and rate-feedback algorithms to further increase channel throughput. This paper provides an analysis of the expected gains due to scheduling and bits needed for rate feedback. Our analysis requires an accurate

Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria

by Tuomas Virtanen - IEEE Trans. On Audio, Speech and Lang. Processing , 2007
"... Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain ..."
Abstract - Cited by 189 (30 self) - Add to MetaCart
Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time
Results 1 - 10 of 1,309