• Call for Nomination of 2007 Best Paper Awards in the area of Signal Processing and Coding for Data Storage 

• 2006 Best Paper Award on Data Storage Area

  Awardees: Jing Jiang and Krishna R. Narayanan

  Paper:  "Iterative Soft-Input Soft-Output Decoding of Reed--Solomon Codes by Adapting the Parity-Check Matrix," IEEE TRANSACTIONS ON INFORMATION THEORY, vol. 52, No. 8, pp. 3746- 3756, August 2006.

   

  Abstract: An iterative algorithm is presented for soft-input soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum-product algorithm (SPA) in conjunction with a binary parity-check matrix of the RS code. The novelty is in reducing a submatrix of the binary parity-check matrix that corresponds to less reliable bits to a sparse nature before the SPA is applied at each iteration. The proposed algorithm can be geometrically interpreted as a two-stage gradient descent with an adaptive potential function. This adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and, therefore, significantly improves the performance. Simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard-decision decoding (HDD) and compare favorably with other popular soft-decision decoding methods.

• 2006 Best Student Paper Award on Data Storage Area

  Awardee: Riccardo Pighi, Riccardo Raheli and Umberto Amadei

  Paper:  "Multidimensional Signal Processing and Detection for Storage Systems with Data-Dependent Transition Noise," IEEE TRANSACTIONS ON MAGNETICS, vol. 42, No. 7, pp. 1905-1916, July 2006.

   

  Abstract: In the last decade, significant research on detection algorithms capable of mitigating the effects of colored Gaussian thermal noise and transition noise in storage systems, has been performed. In this paper, we present a new detection scheme based on a multidimensional detector front end and multidimensional linear prediction, applied to maximum a posteriori probability (MAP) sequence detection. This method improves the bit-error-rate (BER) performance with respect to previous approaches and makes the detector quite insensitive to transition noise. We show that the gain in terms of BER versus signal-to-noise ratio with our detector increases with the user density. The results obtained for a magnetic storage channel are extendable to optical storage systems as well.

   

• IEEE ICC2007 Best Paper Award in Signal Processing and Coding for Storage

Awardees: Richard Todd , J. R. Cruz

Paper: R. Todd and J. R. Cruz, "Computing Maximum-Likelihood Bounds for Reed-Solomon Codes over Partial Response Channels," University of Oklahoma

• Signal Processing for Storage 2005 Best Paper Award

Awardees: Aleksandar Kavcic, Xiao Ma, Nedeljko Varnica

Paper: A. Kavcic, X. Ma, N. Varnica, "Matched Information Rate Codes for Partial Response Channels," IEEE Transactions on Information Theory, vol. 51, pp. 973-989, March 2005

 

Abstract: In this paper, we design capacity-approaching codes for partial response channels. The codes are constructed as concatenations of inner trellis codes and outer low-density parity- check (LDPC) codes. Unlike previous constructions of trellis codes for partial response channels, we disregard any algebraic properties (e.g., the minimum distance or the run-length limit) in our design of the trellis code. Our design is purely probabilistic in that we construct the inner trellis code to mimic the transition probabilities of a Markov process that achieves a high (capacity-approaching) information rate. Hence, we name it a matched information rate (MIR) design. We provide a set of five design rules for constructions of capacity-approaching MIR inner trellis codes. We optimize the outer LDPC code using density evolution tools specially modified to fit the superchannel consisting of the inner MIR trellis code concatenated with the partial response channel. Using this strategy, we design degree sequences of irregular LDPC codes whose noise tolerance thresholds are only fractions of a decibel away from the capacity. Examples of code constructions are shown for channels both with and without spectral s.

• Signal Processing for Storage 2005 Best Student Paper Award

Awardee: Shaohua Yang

Paper: S. Yang, A. Kavcic, S. Tatikonda, "The Feedback Capacity of Finite-State Machine Channels," IEEE Transactions on Information Theory, vol. 51, pp. 799-810, March 2005.

 

Abstract: We consider a finite-state machine channel with a finite memory length (e.g., finite length intersymbol interference channels with finite input alphabets-also known as partial response channels). For such a finite-state machine channel, we show that feedback-dependent Markov sources achieve the feedback capacity, and that the required memory length of the Markov process matches the memory length of the channel. Further, we show that the whole history of feedback is summarized by the causal posterior channel state distribution, which is computed by the sum-product forward recursion of the Bahl-Cocke-Jelinek-Raviv (BCJR) (Baum-Welch, discrete-time Wonham filtering) algorithm. These results drastically reduce the space over which the optimal feedback-dependent source distribution needs to be sought. Further, the feedback capacity computation may then be formulated as an average-reward-per-stage stochastic control problem, which is solved by dynamic programming. With the knowledge of the capacity-achieving source distribution, the value of the capacity is easily estimated using Markov chain Monte Carlo methods. When the feedback is delayed, we show that the feedback capacity can be computed by similar procedures. We also show that the delayed feedback capacity is a tight upper bound on the feedforward capacity by comparing it to tight existing lower bounds. We demonstrate the applicability of the method by computing the feedback capacity of partial response channels and the feedback capacity of run-length-limited (RLL) sequences over binary symmetric channels (BSCs).

• Past Winners of Best Student Paper Award 

          Year 2004:  Zheng Zhang          

Title: "Achievable information rates and coding for MIMO systems over ISI channels and frequency-selective fading channels,"  IEEE Trans. Commun., vol. 52, pp. 1698-1710, Oct. 2004.
        

Abstract: We introduce a simulation-based method to compute the information rates of intersymbol interference (ISI) channels with additive colored Gaussian noise and/or signal-dependent Gaussian noise when the inputs are binary and independent identically distributed. The method extends the idea advanced by Arnold and Loeliger (2001), which focuses on the ISI channels with additive white Gaussian noise (AWGN). With the new method, we can compute the information rates of the Lorentzian channel with media noise that represents a suitable model for practical magnetic recording channels. We illustrate the use of the technique via several examples and show that media noise is preferable to AWGN in terms of the achievable information rates, at low signal-to-noise ratios, in particular. We also present examples of turbo codes for Lorentzian channels with media noise and compare the performance with the achievable information rates. The results demonstrate, not surprisingly, that improved detectors are necessary to achieve the channel capacity for magnetic recording channels when the media noise is dominant.

         Year 2001:  Dieter Arnold          

Title: "On the information rate of binary-input channels with memory,"  ICC 2001, Helsinki, Finland.
        

Abstract: The entropy rate of a finite-state hidden Markov model can be estimated by forward sum-product trellis processing (i.e., the forward recursion of the Baum-Welch/BCJR algorithm) of simulated model output data. This can be used to compute information rates of binary-input AWGN channels with memory.