LMS DD Equalizer: Difference between revisions

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[[Category:Block Docs]]
[[Category:Block Docs]]
[[Category:Stub Docs]]
This block implements an LMS-based decision-directed equalizer.  
This is the template for the [[:Category:Block_Docs|"Page-per-block Docs"]].  This first section should describe what the block does and how to use it, using however many paragraphs necessary.  Note that the title of the wiki page should match the block's name in GRC, i.e. the one defined in the block's .grc file.  Look at the [[FFT]] Block for a good example.


As this is a basic template, it's also in the [[:Category:Stub_Docs|"Stub Docs category"]]. Please improve it.
It uses a set of weights, w, to correlate against the inputs, u, and a decisions is then made from this output. The error in the decision is used to update the weight vector.
        y[n] = conj(w[n]) u[n] d[n] = decision(y[n]) e[n] = d[n] - y[n] w[n+1] = w[n] + mu u[n] conj(e[n])
Where mu is a gain value (between 0 and 1 and usually small, around 0.001 - 0.01.
       
This block uses the digital_constellation object for making the decision from y[n]. Create the constellation object for whatever constellation is to be used and pass in the object. In Python, you can use something like:
        self.constellation = digital.constellation_qpsk()
To create a QPSK constellation (see the digital_constellation block for more details as to what constellations are available or how to create your own). You then pass the object to this block as an sptr, or using "self.constellation.base()".
 
The theory for this algorithm can be found in Chapter 9 of: S. Haykin, Adaptive Filter Theory, Upper Saddle River, NJ: Prentice Hall, 1996.


== Parameters ==
== Parameters ==
(''R''): <span class="plainlinks">[https://wiki.gnuradio.org/index.php/GNURadioCompanion#Variable_Controls ''Run-time adjustable'']</span>
(''R''): <span class="plainlinks">[https://wiki.gnuradio.org/index.php/GNURadioCompanion#Variable_Controls ''Run-time adjustable'']</span>


; Param 1 (''R'')
; Gain (''R'')
: Description of parameter, provide any tips or recommended values. Note that the name of the parameter above should match the param's label that shows up in grc (e.g. Sample Rate).
: Gain of the update loop
 
; Num. Taps
: Number of taps in the equalizer (channel size)
 
; Samples per Symbol
: Number of samples per symbol of the input signal


; Param 2
; Constellation Object
: blah blah blah
: A constellation derived from class 'constellation'.


== Example Flowgraph ==
== Example Flowgraph ==
Line 21: Line 34:


; C++ files
; C++ files
: [https://github.com/gnuradio/gnuradio TODO]
: [https://github.com/gnuradio/gnuradio/blob/master/gr-digital/lib/lms_dd_equalizer_cc_impl.cc]


; Header files
; Header files
: [https://github.com/gnuradio/gnuradio TODO]
: [https://github.com/gnuradio/gnuradio/blob/master/gr-digital/lib/lms_dd_equalizer_cc_impl.h]


; Public header files
; Public header files
: [https://github.com/gnuradio/gnuradio TODO]
: [https://github.com/gnuradio/gnuradio/blob/master/gr-digital/include/gnuradio/digital/lms_dd_equalizer_cc.h]


; Block definition
; Block definition
: [https://github.com/gnuradio/gnuradio TODO]
: [https://github.com/gnuradio/gnuradio/blob/master/gr-digital/grc/digital_lms_dd_equalizer_cc.block.yml]

Revision as of 15:05, 6 September 2019

This block implements an LMS-based decision-directed equalizer.

It uses a set of weights, w, to correlate against the inputs, u, and a decisions is then made from this output. The error in the decision is used to update the weight vector. 

       y[n] = conj(w[n]) u[n] d[n] = decision(y[n]) e[n] = d[n] - y[n] w[n+1] = w[n] + mu u[n] conj(e[n])

Where mu is a gain value (between 0 and 1 and usually small, around 0.001 - 0.01.

This block uses the digital_constellation object for making the decision from y[n]. Create the constellation object for whatever constellation is to be used and pass in the object. In Python, you can use something like: 

       self.constellation = digital.constellation_qpsk()

To create a QPSK constellation (see the digital_constellation block for more details as to what constellations are available or how to create your own). You then pass the object to this block as an sptr, or using "self.constellation.base()".

The theory for this algorithm can be found in Chapter 9 of: S. Haykin, Adaptive Filter Theory, Upper Saddle River, NJ: Prentice Hall, 1996.

Parameters

(R): Run-time adjustable

Gain (R)
Gain of the update loop
Num. Taps
Number of taps in the equalizer (channel size)
Samples per Symbol
Number of samples per symbol of the input signal
Constellation Object
A constellation derived from class 'constellation'.

Example Flowgraph

Insert description of flowgraph here, then show a screenshot of the flowgraph and the output if there is an interesting GUI. Currently we have no standard method of uploading the actual flowgraph to the wiki or git repo, unfortunately. The plan is to have an example flowgraph showing how the block might be used, for every block, and the flowgraphs will live in the git repo.

Source Files

C++ files
[1]
Header files
[2]
Public header files
[3]
Block definition
[4]
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