dfe - Construct decision-feedback equalizer object

Syntax

eqobj = dfe(nfwdweights,nfbkweights,alg) eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst) eqobj = dfe(nfwdweights,nfbkweights,alg,sigconst,nsamp)

Description

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 Using Adaptive Equalizer Functions and Objects.

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.

Properties

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 eqobj, enter reset(eqobj).

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.

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`.

Relationships Among Properties

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.

Examples

An example is in Defining an Equalizer Object.