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Coding Techniques for Channels with Gain and/or Offset Mismatch

Goal: In practical storage and communication systems it is usually found that the transmitted or stored sequences do not only suffer from (Gaussian) noise, but also from an unknown gain and/or offset. Recently, Pearson distance based detection techniques in combination with appropriate (de)coding methods which are immune to gain and offset mismatch were proposed by Immink and Weber. The Pearson distance can only fruitfully be used for sets of q-ary codewords, called Pearson codes, that satisfy specific properties. Pearson codes are simple and have very low redundancy, but the drawback is that they are quite sensitive to errors caused by the noise. Goal of my research is to investigate possible ways of extending the Pearson coding concept to improve its performance in noisy channel conditions, while maintaining the resistance against gain/offset mismatch and the low complexity features. Naturally, several (de)coding techniques and detecting schemes, cardinalities and redundancies of coding techniques and performance evaluation will constitute data in the research.

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Renfei Bu
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We consider noisy data transmission channels with unknown scaling and varying offset mismatch. Minimum Pearson distance detection is used in cooperation with a difference operator, which offers immunity to such mismatch. Pair-constrained codes are proposed for unambiguous decoding, where in each codeword certain adjacent symbol pairs must appear at least once. We investigate the cardinality and redundancy of these codes.
Renfei Bu
added 2 research items
Data storage systems may not only be disturbed by noise. In some cases, the error performance can also be seriously degraded by offset mismatch. Here, channels are considered for which both the noise and offset are bounded. For such channels, Euclidean distance-based decoding, Pearson distance-based decoding, and Maximum Likelihood decoding are considered. In particular, for each of these decoders, bounds are determined on the magnitudes of the noise and offset intervals which lead to a word error rate equal to zero. Case studies with simulation results are presented confirming the findings.
Reliability is a critical issue for modern multi-level cell memories. We consider a multi-level cell channel model such that the retrieved data is not only corrupted by Gaussian noise, but hampered by scaling and offset mismatch as well. We assume that the intervals from which the scaling and offset values are taken are known, but no further assumptions on the distributions on these intervals are made. We derive maximum likelihood (ML) decoding methods for such channels, based on finding a codeword that has closest Euclidean distance to a specified set defined by the received vector and the scaling and offset parameters. We provide geometric interpretations of scaling and offset and also show that certain known criteria appear as special cases of our general setting.
Renfei Bu
added a project goal
In practical storage and communication systems it is usually found that the transmitted or stored sequences do not only suffer from (Gaussian) noise, but also from an unknown gain and/or offset. Recently, Pearson distance based detection techniques in combination with appropriate (de)coding methods which are immune to gain and offset mismatch were proposed by Immink and Weber. The Pearson distance can only fruitfully be used for sets of q-ary codewords, called Pearson codes, that satisfy specific properties. Pearson codes are simple and have very low redundancy, but the drawback is that they are quite sensitive to errors caused by the noise. Goal of my research is to investigate possible ways of extending the Pearson coding concept to improve its performance in noisy channel conditions, while maintaining the resistance against gain/offset mismatch and the low complexity features. Naturally, several (de)coding techniques and detecting schemes, cardinalities and redundancies of coding techniques and performance evaluation will constitute data in the research.