Construct decision-feedback equalizer object
eqobj = dfe(nfwdweights,nfbkweights,alg)
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst)
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst,nsamp)
The dfe
function creates an equalizer object
that you can use with the equalize
function
to equalize a signal. To learn more about the process for equalizing
a signal, see Adaptive Algorithms.
eqobj = dfe(nfwdweights,nfbkweights,alg)
constructs
a decision feedback equalizer object. The equalizer's feedforward
and feedback filters have nfwdweights
and nfbkweights
symbol-spaced
complex weights, respectively, which are initially all zeros. alg
describes
the adaptive algorithm that the equalizer uses; you should create alg
using
any of these functions: lms
, signlms
, normlms
, varlms
, rls
,
or cma
. The signal constellation
of the desired output is [-1 1]
, which corresponds
to binary phase shift keying (BPSK).
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst)
specifies
the signal constellation vector of the desired output.
eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst,nsamp)
constructs
a DFE with a fractionally spaced forward filter. The forward filter
has nfwdweights
complex weights spaced at T/nsamp
,
where T
is the symbol period and nsamp
is
a positive integer. nsamp = 1
corresponds to a
symbol-spaced forward filter.
The table below describes the properties of the decision feedback equalizer object. To learn how to view or change the values of a decision feedback equalizer object, see Accessing Properties of an Equalizer.
Note:
To initialize or reset the equalizer object |
Property | Description |
---|---|
EqType | Fixed value, 'Decision
Feedback Equalizer' |
AlgType | Name of the adaptive algorithm
represented by alg |
nWeights | Number of weights in the
forward filter and the feedback filter, in the format [nfwdweights,
nfbkweights] . The number of weights in the forward filter
must be at least 1. |
nSampPerSym | Number of input samples
per symbol (equivalent to nsamp input argument).
This value relates to both the equalizer structure (see the use of
K in Decision-Feedback Equalizers) and an assumption about
the signal to be equalized. |
RefTap (except
for CMA equalizers) | Reference tap index, between
1 and nfwdweights . Setting this to a value greater
than 1 effectively delays the reference signal with respect to the
equalizer's input signal. |
SigConst | Signal constellation, a vector whose length is typically a power of 2. |
Weights | Vector that concatenates the complex coefficients from the forward filter and the feedback filter. This is the set of w_{i} values in the schematic in Decision-Feedback Equalizers. |
WeightInputs | Vector that concatenates the tap weight inputs for the forward filter and the feedback filter. This is the set of u_{i} values in the schematic in Decision-Feedback Equalizers. |
ResetBeforeFiltering | If 1 ,
each call to equalize resets the state of eqobj before
equalizing. If 0 , the equalization process maintains
continuity from one call to the next. |
NumSamplesProcessed | Number of samples the equalizer
processed since the last reset. When you create or reset eqobj ,
this property value is 0 . |
Properties specific to the
adaptive algorithm represented by alg | See reference page for the
adaptive algorithm function that created alg : lms , signlms , normlms , varlms , rls , or cma . |
If you change nWeights
, MATLAB maintains
consistency in the equalizer object by adjusting the values of the
properties listed below.
Property | Adjusted Value |
---|---|
Weights | zeros(1,sum(nWeights)) |
WeightInputs | zeros(1,sum(nWeights)) |
StepSize (Variable-step-size
LMS equalizers) | InitStep*ones(1,sum(nWeights)) |
InvCorrMatrix (RLS
equalizers) | InvCorrInit*eye(sum(nWeights)) |
An example illustrating relationships among properties is in Linked Properties of an Equalizer Object.