Project

Detection methods

Goal: Detection of noisy encoded signals transmitted over channels whose characteristics, such as gain or offset, are not completely known. Conventional Euclidean-distance-based detection fails if the mismatch between the actual and modeled channel is too large. We have been searching for alternative detection methods that are more resilient in the face of mismatch. Pearson-distance-based detection or clustering methods have been investigated as alternatives.

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Kees Schouhamer Immink
added 2 research items
Maximum likelihood (ML) decision criteria have been developed for channels suffering from signal independent offset mismatch. Here, such criteria are considered for signal dependent offset, which means that the value of the offset may differ for distinct signal levels rather than being the same for all levels. An ML decision criterion is derived, assuming uniform distributions for both the noise and the offset. In particular, for the proposed ML decoder, bounds are determined on the standard deviations of the noise and the offset which lead to a word error rate equal to zero. Simulation results are presented confirming the findings.
Decoders minimizing the Euclidean distance between the received word and the candidate codewords are known to be optimal for channels suffering from Gaussian noise. However, when the stored or transmitted signals are also corrupted by an unknown offset, other decoders may perform better. In particular, applying the Euclidean distance on normalized words makes the decoding result independent of the offset. The use of this distance measure calls for alternative code design criteria in order to get good performance in the presence of both noise and offset. In this context, various adapted versions of classical binary block codes are proposed, such as (i) cosets of linear codes, (ii) (unions of) constant weight codes, and (iii) unordered codes. It is shown that considerable performance improvements can be achieved, particularly when the offset is large compared to the noise.
Kees Schouhamer Immink
added a research item
We consider noisy communications and storage systems that are hampered by varying offset of unknown magnitude such as low-frequency signals of unknown amplitude added to the sent signal. We study and analyze a new detection method whose error performance is independent of both unknown base offset and offset’s slew rate. The new method requires, for a codeword length n ≥ 12, less than 1.5 dB more noise margin than Euclidean distance detection. The relationship with constrained codes based on mass-centered codewords and the new detection method is discussed.
Kees Schouhamer Immink
added a research item
In many channels, the transmitted signals do not only face noise, but offset mismatch as well. In the prior art, maximum likelihood (ML) decision criteria have already been developed for noisy channels suffering from signal independent offset . In this paper, such ML criterion is considered for the case of binary signals suffering from Gaussian noise and signal dependent offset . The signal dependency of the offset signifies that it may differ for distinct signal levels, i.e., the offset experienced by the zeroes in a transmitted codeword is not necessarily the same as the offset for the ones. Besides the ML criterion itself, also an option to reduce the complexity is considered. Further, a brief performance analysis is provided, confirming the superiority of the newly developed ML decoder over classical decoders based on the Euclidean or Pearson distances.
Kees Schouhamer Immink
added a research item
We report on the feasibility of k-means clustering techniques for the dynamic threshold detection of encoded q-ary symbols transmitted over a noisy channel with partially unknown channel parameters. We first assess the performance of k-means clustering technique without dedicated constrained coding. We apply constrained codes which allows a wider range of channel uncertainties so improving the detection reliability.
Kees Schouhamer Immink
added 13 research items
The reliability of mass storage systems, such as optical data recording and non-volatile memory (Flash), is seriously hampered by uncertainty of the actual value of the offset (drift) or gain (amplitude) of the retrieved signal. The recently introduced minimum Pearson distance detection is immune to unknown offset or gain, but this virtue comes at the cost of a lessened noise margin at nominal channel conditions. We will present a novel hybrid detection method, where we combine the outputs of the minimum Euclidean distance and Pearson distance detectors so that we may trade detection robustness versus noise margin. We will compute the error performance of hybrid detection in the presence of unknown channel mismatch and additive noise.
Coding techniques are shown to provide an effective means of controlling the effects of bandwidth and gain variation associated with space losses. The combination of a specific channel code and a suitable partial-response detection technique is proposed and studies for the purpose of obtaining enhanced robustness. The technique presented for encoding and decoding digital audio signals offers the advantage of a graceful degradation of the performance when the signal is recorded on a digital recorder with a wide range of bandwidths. This situation may arise, for example, when contact between head and medium is insufficient. The technique can also be used for digital video signals or any other information that might carry significance information
Kees Schouhamer Immink
added a project goal
Detection of noisy encoded signals transmitted over channels whose characteristics, such as gain or offset, are not completely known. Conventional Euclidean-distance-based detection fails if the mismatch between the actual and modeled channel is too large. We have been searching for alternative detection methods that are more resilient in the face of mismatch. Pearson-distance-based detection or clustering methods have been investigated as alternatives.