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This toolbox supports these distinct classes of equalizers, each with a different overall structure:
Linear equalizers, a class that is further divided into these categories:
Symbol-spaced equalizers
Fractionally spaced equalizers (FSE)
Decision-feedback equalizers (DFE)
MLSE (Maximum-Likelihood Sequence Estimation) equalizer that uses the Viterbi algorithm. To learn how to use the MLSE equalizer capabilities, see Using MLSE Equalizers.
Linear and decision-feedback equalizers are adaptive equalizers that use an adaptive algorithm when operating. For each of the adaptive equalizer classes listed above, this toolbox supports these adaptive algorithms:
Least mean square (LMS)
Signed LMS, including these types: sign LMS, signed regressor LMS, and sign-sign LMS
Normalized LMS
Variable-step-size LMS
Recursive least squares (RLS)
Constant modulus algorithm (CMA)
To learn how to use the adaptive equalizer capabilities, start with Using Adaptive Equalizer Functions and Objects. For brief background material on the supported adaptive equalizer types, see Overview of Adaptive Equalizer Classes. For more detailed background material, see the works listed in Selected Bibliography for Equalizers.
| | Equalizers | Overview of Adaptive Equalizer Classes | |
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