Difference between revisions of "Linear Equalizer"

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Latest revision as of 14:56, 8 April 2020

Performs linear equalization on a stream of complex samples.

The Linear Equalizer block equalizes the incoming signal using an FIR filter. If provided with a training sequence and a training start tag, data aided equalization will be performed starting with the tagged sample. If training-based equalization is active and the training sequence ends, then optionally decision directed equalization will be performed given the adapt_after_training If no training sequence or no tag is provided, decision directed equalization will be performed

This equalizer decimates to the symbol rate according to the samples per symbol parameter

Parameters

(R): Run-time adjustable

Num Taps
Number of taps for the FIR filter
SPS
Samples per Symbol of the input stream. The output will be downsampled to the symbol rate relative to this parameter
Alg
Adaptive algorithm object. This is the heart of the equalizer, it controls how the adaptive weights of the linear equalizer are updated
Training Sequence
Sequence of samples that will be used to train the equalizer. Provide empty vector to default to DD equalizer
Adapt After Training
Flag that when set true, continue DD training after training on specified training sequence
Training Start Tag
String to specify the start of the training sequence in the incoming data


Example Flowgraph

The included example, linear_equalizer_compare.grc shows the equalization of a modulated sequence passed through a linear channel.

First a sequence of a known preamble followed by random data are modulated using the Constellation Modulator, then passed through a Channel Model which simulates a multipath channel with AWGN. Next, a correlation estimator is used to find the preamble and tag the stream where it was found.

Two linear equalizer blocks are used to compare the performance of training using the known preamble versus entirely decision directed mode

Linear equalizer compare.grc.png

Linear equalizer compare.grc output.png

We can see in the resulting graphs that the training sequence based equalizer converged more quickly


Source Files