Publications

Fully Parallel Stochastic LDPC Decoders

Saeed Sharifi Tehrani

Shie Mannor

Warren J. Gross

Stochastic decoding is a new approach to iterative decoding on graphs. This paper presents a hardware architecture for fully parallel stocha… (voir plus)stic low-density parity-check (LDPC) decoders. To obtain the characteristics of the proposed architecture, we apply this architecture to decode an irregular state-of-the-art (1056,528) LDPC code on a Xilinx Virtex-4 LX200 field-programmable gate-array (FPGA) device. The implemented decoder achieves a clock frequency of 222 MHz and a throughput of about 1.66 Gb/s at

2008-10-31

IEEE Transactions on Signal Processing (publié)

doi.org

Stochastic Decoding of Linear Block Codes With High-Density Parity-Check Matrices

Saeed Sharifi Tehrani

Christophe Jego

Bo Zhu

Warren J. Gross

This correspondence extends the application of the recently proposed stochastic decoding approach to decode linear block codes with high-den… (voir plus)sity parity-check matrices and discusses its hardware complexity. Results demonstrate decoding performance close to floating-point iterative soft-input soft-output (SISO) decoding while offering nodes with considerably lower complexity compared to fixed-point SISO decoding.

2008-10-31

IEEE Transactions on Signal Processing (publié)

doi.org

Distributed Average Consensus With Dithered Quantization

Tuncer Can Aysal

Mark J. Coates

Michael G. Rabbat

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints… (voir plus) or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.

2008-09-30

IEEE Transactions on Signal Processing (publié)

doi.org

Recent Advances in Reinforcement Learning

Joelle Pineau

2007-12-31

Lecture Notes in Computer Science (publié)

doi.org

Advances in Information Retrieval

Diane Kelly

Fernando Diaz

Nicholas J. Belkin