10-04-2017, 07:48 PM
Time-dispersive channels can cause intersymbol interference (ISI), a form of distortion that causes symbols to overlap and become indistinguishable by the receiver. For example, in a multipath scattering environment, the receiver sees delayed versions of a symbol transmission, which can interfere with other symbol transmissions. An equalizer attempts to mitigate ISI and improve receiver performance. Communications System Toolbox provides equalization capabilities using one or more Simulink blocks, System objects, or MATLAB functions.
This product supports the following distinct classes of equalizers, each of which have a different overall structure:
Linear equalizers, a class that is further divided into these categories:
Symbol-spaced equalizers
Fractionally spaced equalizers (FSEs)
Decision-feedback equalizers (DFEs)
MLSE (Maximum-Likelihood Sequence Estimation) equalizers that uses the Viterbi algorithm. To learn how to use the MLSE equalizer capabilities, see 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)
Several blocks from the Equalizers library implement adaptive equalizers, differing in the equalizer structure and the type of adaptive algorithm that they use. In all cases, you specify information about the equalizer structure (such as the number of taps), the adaptive algorithm (such as the step size), and the signal constellation used by the modulator in your model. You also specify an initial set of weights for the taps of the equalizer; the block adaptively updates the weights throughout the simulation. For adaptive algorithms other than CMA, the equalizer can adapt the weights in two modes: training mode and decision-directed mode.
