Decision-feedback equalizer and method of updating filter coefficients

Exemplary embodiments of the present invention provide an equalizer combined with a decoder and a method of updating filter coefficients. The method may include calculating output error signals ek, multiplying the output error signals by a parameter, obtaining a partial value by multiplying a delayed decoder decision stored in a filter delay line corresponding to an i-th filter coefficient by the result obtaining a partial value by multiplying a constant by a feedback coefficient and obtaining an updated value by adding the two partial values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This U.S. nonprovisional patent application claims priority under 35 U.S.C. §119 of Korean Patent Application 2004-7530 filed on Feb. 5, 2004, the entire contents of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Exemplary embodiments of the present invention relate to digital video broadcasting, and more particularly, to a decision-feedback equalizer, which may receive a variety of digital information and a method of updating coefficients thereof.

2. Description of the Conventional Art

An equalizer for digital video broadcasting may be needed for large amounts of digital information, for example, voice, data, and/or video communications. Such digital information may be transmitted via various transmission mediums, which may have different transmission characteristics. Transmission mediums may cause different kinds of ghosting, for example, frequency-dependent phase, amplitude distortion, multipath receiving, or voice echoes, and various types of fading in signals, for example, Rayleigh fading. Data transmissions may suffer from a noise, for example, additive white Gaussian noise. The equalizer may be used to reduce echoes and/or video ghosts and control signals for wireless modems and/or telephones.

In digital communications, data transmission over intersymbol interference (ISI) channels may be a problem. The ISI may occur when pulsed information, for example, amplitude-modulated digital transmissions, may be transmitted over analog channels, for example, telephone lines and/or skywave channels.

Maximum-likelihood sequence estimation (MLSE) may achieve an improved symbol error rate (SER), but may become more complex with the length of the channel time dispersion. Extremely high complexity of the MLSE in a software and/or hardware may limit its use.

A linear equalizer (LE) may detect and equalize ISI. LE may have a complexity, which may be a linear function of the channel dispersion length and may suffer from significant noise enhancement. The performance of the linear equalizer may be worse than the performance of an MLSE.

A decision-feedback equalizer (DFE) may have a lower complexity and/or improved performance.

FIG. 1is an example of a construction diagram of a conventional DFE. The DFE may use previously decoded data symbols in order to calculate and reduce intersymbol interference (ISI). The performance of the DFE may be degraded due to incorrect decisions in a decision feedback filter, for example, when a channel introduces strong ghosts, for example, during a single frequency network operation in digital television broadcasting.

Referring toFIG. 1, a DFE may include a feedforward filter102, a feedback filter103, a slicer104, and an adder105. A received digital signal101may be input to the feedforward filter102.

The feedforward filter102may partially correct signal errors using a filter having a magnitude opposite to a magnitude of the input digital signal101. The slicer104may be, for example, a decision device which may be based on the magnitudes of received signals and may classify received signals based on decisions of 0, ±2, ±4, and ±6 in order. The received signals may be classified into symbols corresponding to normalized signals of ±1, ±3, ±5, and ±7. The slider104may be a multi-dimensional slicer, which may be used in, for example, quadrature amplitude modulation (QAM) systems.

The adder105may add the output of the feedforward filter102and the output of the feedback filter103and may output the result to the slicer104. The feedforward filter102may reduce noises using a filter having a magnitude opposite to a magnitude of the input digital signal101.

A decision-feedback sequence estimation (DFSE) algorithm may provide a tradeoff between performance and complexity.

Wireless communication systems employ trellis-coded modulation (TCM).

FIG. 2illustrates a conventional TCM scheme for 8-level amplitude modulated signals.

Referring toFIG. 2, a TCM encoder may be comprised of an 8-VSB trellis encoder201and an 8-level symbol mapper203. The 8-VSB trellis encoder201may employ an 8-level 3-bit 1-dimensional arrangement. The 8-VSB trellis encoder may use a ⅔ rate trellis code.

A method for detection of trellis-coded symbols in channels without ISI may be the MLSE. The number of trellis states in codes used for TCM may be smaller and the complexity of the MLSE may not be higher. The MLSE may be implemented using a Viterbi algorithm (or Viterbi decoding algorithm). The TCM symbols transmitted through ISI-free channels may be detected with improved performance.

When channels introduce ISI, the MLSE detector which takes into account the ISI introduced by the channels and the TCM may become more complex. A DFE may be used for the compensation of channel ISI and a MLSE (Viterbi) decoder to decode the TCM.

FIG. 3is a construction diagram of a conventional DFE combined with a TCM decoder.

Referring toFIG. 3, the DFE combined with the TCM decoder may comprise a feedforward filter302, a feedback filter303, a slicer304, and an adder305, and a TCM decoder307which may decode trellis-coded symbols. A received digital signal may be input to the DFE via an input line301and output via an output line306connected to the TCM decoder307.

The DFE may be operated before the TCM decoder uses uncoded symbols to perform a feedback operation and the reliability of the uncoded symbols may be lower. The performance may be worse than that of a joint (channel+TCM) MLSE.

FIG. 4is a construction diagram of another conventional DFE combined with a TCM decoder.

The DFSE algorithm may be used to decode TCM symbols transmitted through ISI channels.

Instead of using slicer decisions in a feedback filter, the DFE may use symbol decisions from the more likely surviving path of the Viterbi decoder. This scheme, sometimes referred to as “a Viterbi decoder with global decision feedback”, is illustrated inFIG. 4. Referring toFIG. 4, an adder407may add the output of a feedforward filter402and the output of a feedback filter403and may output the result to a TCM decoder404. The TCM decoder404may decode symbols405and inputs the decoded symbols405to the feedback filter403. The Viterbi decoder with global decision feedback may use the symbol decisions from the more likely surviving path of the TCM (Viterbi) decoder404as the inputs of the feedback filter403. A decoding depth Nth symbol, which may be the more reliable symbol among the outputs of the TCM decoder404, may become an output signal406.

This combination of a DFE and a TCM (Viterbi) decoder, as shown inFIG. 4, may have improved performance over the scheme shown inFIG. 3, since the decisions from the TCM (Viterbi) decoder may be more reliable.

FIG. 5is a construction diagram of a conventional DFE, which may use a least-mean square (LMS) algorithm for updating feedback filter coefficients.

In, for example, wireless applications of MLSE and DFE, the channel transfer function may be unknown at the receiver and/or time-variant. Any detection/equalization scheme used in wireless communication receivers may be adaptive, i.e., may be able to change coefficients of an equalizer and track channel variations. In the LMS scheme, equalizer coefficients may be recursively updated at every iteration of the algorithm. For example, feedback filter coefficients of a DFE may recursively be updated in accordance with an LMS algorithm as shown in Equation 1.
bi(k+1)=bi(k)+μek{circumflex over (d)}k−i, i=1, 2, . . . , LB(1)

Here, bi(k)are i-th feedback coefficients (518,519, . . . , and520) of a DFE at k-th iteration, LBis the number of feedback filter coefficients, {circumflex over (d)}kare decisions in a feedback filter, stored in delay lines (521,522, . . . , and523), μ is a step-size parameter (positive constant), and ekare error signals508, which may be differences between the outputs524of the DFE and the decisions {circumflex over (d)}k525. During a training period, the transmitted data sequence dkmay be known and may be used by an equalizer to update the coefficients bi(k)in accordance with the LMS algorithm shown in Equation 1.

A DFE, which may use the LMS adaptation scheme embodied by Equation 1, is shown inFIG. 5. That is,FIG. 5illustrates an example of applying the LMS adaptation scheme shown in Equation 1 to the DFE using the slicer ofFIG. 1. After a training period, decisions {circumflex over (d)}kat the output of a slicer510may be more reliable and the decisions {circumflex over (d)}kmay be used to update equalizer coefficients in accordance with the LMS algorithm.

FIG. 6is a construction diagram of a conventional DFE, which may use both the LMS algorithm and a Stop-and-Go algorithm to update feedback filter coefficients.

The LMS algorithm may also be used without a training sequence.

The “Stop-and-Go” algorithm may disable adaptation if decisions are not reliable, and may update equalizer coefficients if the decisions are more likely to be correct. Detection of less reliable decisions and/or generation of enable/disable flags623may be performed in a ‘Stop-and-Go’ (SAG) block618.

FIG. 7is a construction diagram of a conventional DFE combined with a trellis decoder, which may use an LMS adaptation algorithm to update feedback filter coefficients.

The LMS algorithm shown in Equation 1 and its ‘Stop-and-Go’ variant may be used with the DDFSE scheme shown inFIG. 4.

The combined equalizer/decoder structure with the LMS adaptation algorithm shown inFIG. 7may introduce an instability problem. An example of the instability of this scheme is illustrated inFIG. 8.

FIG. 8is a graph showing the signal-to-noise ratio (SNR) versus the number of iterations for the DFE shown inFIG. 7.

FIG. 8shows the simulation results for the DFE combined with the TCM Viterbi decoder shown inFIG. 7. A channel may have three equal, or substantially equal, amplitude paths and a transmission system may use 8-level amplitude modulated signals as shown inFIG. 2. The equalizer steady-state operation shown inFIG. 8may not depend on resolution of the equalizer coefficients or overflow effects and may be a result of the TCM decision feedback properties.

For first periods of time, the DFE may be operated such that signal-to-noise ratio (SNR) may be more stable. After some periods of time, the stability of the SNR may be greatly lower and a variation thereof may be repeated periodically as the number of iterations increases.

In a convergence region, contribution of the decision-feedback part of the equalizer in ISI compensation may be insignificant, since decisions may be less reliable and the equalizer ofFIG. 7may not rely on the decision feedback mechanism. Decision errors may not affect equalizer stability in this region, and output signal-to-noise ratio (SNR) may be more stable.

After some period of time when decisions become more reliable, the equalizer may rely on these decisions and may use a feedback filter for ISI compensation.

FIG. 9Ais a graph showing an example of the percentage of decision errors in the slicer and the TCM decoder as a function of time.

The TCM decision errors may be correlated and may group in error bursts rather than be distributed randomly as in the case of slicer decisions.

That is, if an error occurs at the output of the TCM decoder, the error may cause series (or bursts) of errors, and for some period of time the number of decision errors in a feedback filter may be higher.

FIG. 9Ashows an example relationship between the percentage of errors in the decision feedback filter and a time. In case of using the TCM feedback scheme shown inFIG. 7, the number of decision errors in the feedback filter may be smaller, but sometimes the TCM decoder may introduce bursts of errors and the number of decision errors may increase.

In case of using the slicer shown inFIG. 5, the percentage of decision errors may be more stable (e.g., approximately 20%). A higher percentage of decision errors may decrease the overall equalizer performance, but may stabilize the adaptation scheme because the LMS algorithm may not rely as much on the feedback mechanism.

When the TCM feedback mechanism is used, the feedback filter may be free of errors, and the LMS algorithm may update equalizer coefficients in accordance with this error-free state of the feedback filter. The equalizer may rely on feedback ISI compensation and may become more sensitive to decision errors. The TCM decoder may introduce bursts of errors and the equalizer performance may be degraded as shown inFIG. 8.

In the ‘Stop-and-Go’ LMS algorithm, the adaptive scheme implemented may disable adaptation when decisions may be less reliable.

SUMMARY OF THE INVENTION

Exemplary embodiments of the present invention provide a method of updating feedback coefficients of an equalizer (e.g., a decision feedback equalizer (DFE)) combined with a decoder (e.g., a trellis decoder), which may reduce negative effects of error propagation at the output of the decoder and may provide smoother and more stable steady-state operation of the equalizer combined with the decoder as compared with conventional adaptation methods.

An exemplary embodiment of the present invention provides an equalizer (e.g., a decision feedback equalizer). The equalizer may include a decoder and a filter. The filter may further include a plurality of filter update units, which may receive a value and decisions from the decoder and may update filter coefficients. The filter update units may further include a first multiplier, which may multiply a decision of the decoder by a value, a second multiplier, which may multiply the filter coefficient by a constant, and an adder, which may add the output of the first multiplier and the output of the second multiplier and update the filter coefficient.

Another exemplary embodiment of the present invention provides a method for updating filter coefficients. The method may include calculating output error signals, multiplying the output error signals by a parameter, obtaining a partial value, obtaining another partial value, and updating by adding the partial values.

Another exemplary embodiment of the present invention provides a filter. The filter may include updaters for updating filter coefficients. The filter updaters may include a first multiplier, a second multiplier, and an adder. The first multiplier may multiply a decision output from a decoder and a first value. The second multiplier may multiply a filter coefficient by a constant. The first adder may add an output of the first multiplier and an output of the second multiplier and may update a filter coefficient.

Another exemplary embodiment of the present invention provides an updater. The updater may include a first multiplier, which may multiply a decision and a first value and a second multiplier which may multiply a filter coefficient by a constant. A first adder may add an output of the first multiplier and an output of the second multiplier and update a filter coefficient.

Another exemplary embodiment of the present invention provides a logic selector. The logic selector may include squarers, which may square values of filter coefficients, multipliers, which may multiply squared filter coefficients generated by the squarers by constants, an adder, which may add outputs of the multipliers, an accumulator, which may accumulate an output of the adder, and a comparator, which may compare values output from the accumulator with threshold values and select constants based on the comparison.

Another exemplary embodiment of the present invention provides a method of updating a filter coefficient. The method may include multiplying a decision with a first value to generate a first result, multiplying a filter coefficient by a constant to generate a second result, adding the first and second results and updating a filter coefficient.

Another exemplary embodiment of the present invention provides a method of selecting a constant. The method may include squaring filter coefficient value, multiplying squared filter coefficients with constants, adding the multiplied values, accumulating the added values, comparing the accumulated values with threshold values and selecting constants based on the comparison.

In exemplary embodiments of the present invention, the decoder may be a trellis coded modulation (TCM) decoder.

In exemplary embodiments of the present invention, the equalizer may further include a second adder, which may subtract an output of the equalizer from the decision of the decoder and may generate an error signal. A third multiplier, which may multiply an error signal generated from the second adder by a parameter and may generate the first value.

In exemplary embodiments of the present invention, the parameter may be a stepsize parameter.

In exemplary embodiments of the present invention, the filter may further include a first and second group of cells, an updater, and an adder. The first group of cells may receive decisions output from the decoder and the outputs of the first multiplier. The second group of cells may receive values obtained by delaying a symbol decision of the decoder. The updater may update filter coefficients. The adder may add outputs from the first group of cells and the second group of cells. Further, each of the first and second groups of cells may include the updater and a coefficient multiplier, which may multiply the decision by the filter coefficient and may generate a result.

In exemplary embodiments of the present invention, the updater may update the filter coefficients in accordance with the equation bi(k+1)=αibi(k)+μekdk−i, i=1, 2, . . . , LB. αimay be constants, which may be proportional to the reliability of the symbol decisions that correspond to an i-th traceback depth. bi(k)may be i-th coefficients of the equalizer at a k-th iteration. LB may be a number of filter coefficients, {circumflex over (d)}kmay be decisions in a filter, μ may be a parameter, and ek may be error signals.

In exemplary embodiments of the present invention, a logic selector may calculate a set of constants, which may satisfy the equation αi=(1+μE└ui2┘)−1. E└ui2┘ may be a variance of a plurality of symbol decision errors. The symbol decision errors may correspond to the i-th depth.

In exemplary embodiments of the present invention, the logic selector may be included in the filter.

In exemplary embodiments of the present invention, the coefficients may satisfy an inequality and i may be an i-th filter coefficient.

In exemplary embodiments of the present invention, the equalizer may include another filter and the decoder may be a Viterbi decoder.

In exemplary embodiments of the present invention, the decoder may have N+1 traceback depths, and the filter may include cells and an adder for adding the outputs from the cells. Each N+1th group of 12 reliability coefficients may have an N+1 th value, which may correspond to an N-th traceback depth. Each decision, which may correspond to an Nth traceback depth may be input to an N+1 th group of delay lines and each of the cells may include an updater and a coefficient multiplier for multiplying each decision by the filter coefficient and generate a result.

In exemplary embodiments of the present invention, the decoder may be a Viterbi decoder.

In exemplary embodiments of the present invention, the equalizer may further include a stop-and-go (SAG) unit. The SAG unit may receive decisions, detect unreliable decisions, and generate enable/disable signals of the algorithm such that the SAG unit may disable adaptation if the decisions are not reliable, and may update filter coefficients if the decisions are reliable.

In exemplary embodiments of the present invention, the method may be repeated for a plurality of filter coefficients.

In exemplary embodiments of the present invention, the method may further include determining a variance, multiplying the variance by a parameter, and obtaining a constant by taking the reciprocal of the sum of the result and 1.

In exemplary embodiments of the present invention, the method may further include squaring the filter coefficients, multiplying the squared values by constants, adding and accumulating the results, and comparing the accumulated values with threshold values to select a constant based on the comparison.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS OF THE PRESENT INVENTION

Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings, in which exemplary embodiments of the present invention are shown. The same reference numerals are used to denote the same elements throughout the drawings.

FIG. 9Bis a simplified model of the graph shown inFIG. 9A, which may enable a stable adaptation algorithm according to exemplary embodiments of the present invention.

Adaptation may be performed using decisions in a feedback filter as shown inFIG. 9B, which may have less frequent errors (e.g., may be error-free). The algorithm may operate with reduced errors (e.g., mean-square errors (MSE)), when the feedback filter includes decision errors (e.g., during error bursts), in order to reduce SNR degradation as shown inFIG. 8.

The development of the stable adaptation algorithm may be based on the above-described model and adaptive filtering. An improved algorithm may reduce the cost function as illustrated in equation 2.
J=E└(yk−dk)2┘  (2)

ykmay be an output signal of an adder707of an equalizer713(e.g., a decision feedback equalizer (DFE)), i.e., the sum of the output signal of the filter (e.g., feedforward filter)721and the output signal of the filter (e.g., feedback filter)722, and dkmay be error-reduced transmitted symbols (e.g., error-free transmitted signals). Reduction of the function (2), which may be a cost function, may lead to a modified LMS algorithm shown in Equation 3.
bi(k+1)=αibi(k)+μekdk−i, (i=1, 2, . . . LB)  (3)
αimay be constants (0<αi<1), which may be proportional to the reliability of the decoder (e.g., TCM decoder) symbol decisions, which may correspond to i-th traceback depth. As values i increase, decisions at the output of a TCM decoder may be more reliable and the values αimay be closer to 1. As values i decrease, the decisions may be less reliable and the values αimay be smaller, for example, α1≦α2≦ . . . ≦αLfB. Values of αimay be based on a step-size parameter μ. The values αimay be represented as shown in Equation 4.
αi=(1+μE└ui2┘)−1(4)
E└ui2┘ may be a variance of the decoder (e.g., TCM decoder) symbol decision errors, which may correspond to the i-th depth. The values E└ui2┘ may be found by, for example, simulations or calculations (e.g., theoretical calculations). For example, values E└ui2┘ for a decoder and three-path equal amplitude channel model are given in Table 1.

FIG. 10Ais an example of a construction diagram of an equalizer (e.g., a DFE), which may be combined with a trellis decoder, which may update feedback filter coefficients according to exemplary embodiments of the present invention.

The structure of a decoder (e.g., an adaptive TCM decoder) combined with an equalizer (e.g., a DFE), which may that use the algorithm shown in Equation 3 is illustrated inFIG. 1A. Referring toFIG. 10A, the equalizer may include a filter (e.g., a feedforward filter)1021, a filter (e.g., a feedback filter)1022, an adder1007, and the decoder (e.g., a TCM decoder)1019. In the equalizer shown inFIG. 10A, updating of the filter coefficients (e.g., feedback filter coefficients) in Equation 3 may be performed by multipliers1010_1through1010_5, adders1011_1through1011_5, delay lines1012_1through1012_5, and multipliers1009_1through1009_5. The delay lines1012_1through1012_5may store the filter coefficient value bi(k)and may calculate the filter coefficient value bi(k+1).

The filter1022may include a plurality of filter cells (e.g., feedback filter cells) and an adder. The filter1022may include a first group of cells, a second group of cells, an adder1008, and a logic selector1023. The first group of cells may receive decisions output from the decoder1019and output signals from a first multiplier. Each of the cells of the second group may receive a value which may be obtained by delaying a symbol decision (e.g., a last symbol decision) of the decoder1019and the output signal of the first multiplier. Each of the cells of the second group may include an updater (e.g., a feedback filter coefficient updater), which may update filter coefficients (e.g., feedback filter coefficients). The adder1008may add the outputs of the first and second groups of cells. The logic selector1023may calculate constants from the filter coefficients.

The cells of the first and second groups may include the updater and a coefficient multiplier1013—i, which may multiply the decisions and the filter coefficients and may output the results.

Referring toFIG. 10A, bi(k)(i=1, 2, . . . , LB) may be i-th feedback coefficients of the equalizer at k-th iteration, LBmay be the number of filter coefficients, {circumflex over (d)}kmay be the decisions in the filter1022, which may be stored in delay lines1012_1through1012_5, μ may be a smaller step-size parameter (e.g., positive constant), and ekmay be error signals508, which may be differences between the outputs1015of the equalizer and the decisions {circumflex over (d)}k.

FIG. 10Bis an example of a circuit diagram of a partial circuit of updating i-th feedback coefficients shown inFIG. 10A, according to an exemplary embodiment of the present invention.

Referring toFIG. 10B, an adder1016may generate an error signal ek, which may be a difference between the output1015of the equalizer and the decision {circumflex over (d)}k. A multiplier1017may multiply the error signal ekby the step-size parameter μ. The multiplier1010—imay multiply the result by the decision {circumflex over (d)}kand may generate μekdk−i. The multiplier1009-imay multiply the constant αiand the i-th feedback filter coefficient bi(k)of the equalizer at the k-th iteration. The adder1011—imay add the output of the delay line1012—iand the output of the multiplier1010—iand may generate an i-th feedback filter coefficient bi(k+1), which may correspond to the next (k+1) iteration of the equalizer.

As described above, αimay be decision constants (0<αi<1), which may be proportional to the reliability of decoder symbol decisions, which may correspond to the i-th traceback depth. The decision constants αimay be calculated by the logic selector1023shown inFIGS. 10A and 10B.

The constants (0<α1≦α2≦ . . . ≦αLfB≦1) may be based on a channel profile (e.g., a multipath channel profile). For example, if a channel has several stronger ghosts which may not be compensated by the filter1021, the equalizer may become increasingly sensitive to the reliability of decisions in the filter1022. In order to decrease equalizer instability, smaller values may be assigned to the constants αi. If a channel does not introduce, for example, isolated strange ghosts, the equalizer may not exhibit an instability problem, and smaller values of αimay, for example, degrade equalizer performance.

This degradation may be insignificant and constants α1, α2, . . . , αLBmay be selected as a trade-off between performance in stronger ghost channels and weaker ghost channels. The set of constants α1, α2, . . . , αLBmay be selected (e.g., adaptively selected) in accordance with channel statistics and may improve the performance of the equalizer.

FIG. 10Cis an example of a construction diagram of a logic selector, which may select a set (e.g., an optimal set) of constants (e.g., reliability constants).

The added values may be accumulated in an accumulator1507and noise effects may be reduced. The accumulation time may be, for example, several hundreds of symbols. The accumulated values may be compared with several threshold values in a comparator1508and a set of constants α1, α2, . . . , αLfBmay be selected based on the result of this comparison.

FIG. 11is an example of a construction diagram of an encoder (e.g., a TCM encoder) and an interleaver.

An adaptation algorithm according to exemplary embodiment of the present invention may be applied to systems, which may employ multiple encoders (e.g., TCM encoders) and/or interleavers, for example, an 8-VSB trellis-coded system, which may be used for, for example, digital video broadcasting. Such systems may use a plurality of encoders (e.g., identical TCM encoders). An example construction of these encoders, according to an exemplary embodiment of the present invention is illustrated inFIG. 11.

FIG. 12illustrates another adaptive decoder (e.g., TCM decoder) combined with an equalizer (e.g., a DFE), which may include a de-interleaver.

A filter (e.g., a feedback filter)1224of an equalizer (e.g., a DFE) according to another exemplary embodiment of the present invention illustrated inFIG. 12, may include a plurality of groups of cells, an adder1213and a logic selector1231. Each group of cells may include, for example, 12 cells. The adder1213may add the outputs of the cells and a logic selector1231may calculate constants (e.g., reliability constants).

The cells of the groups may receive values which may be obtained by delaying (e.g., sequentially delaying) decisions at the output of a decoder (e.g., a TCM decoder)1216and error signals of the equalizer, which may be multiplied by a step-size parameter μ. Each of the cells of the groups may include an updater, which may update the filters (e.g., feedback filters). The cells of the groups may also include a multiplier1211, which may multiply filter coefficients (e.g., feedback filter coefficients by input decisions.

An updater (e.g., feedback filter updater) may include a multiplier1208, a multiplier1210, a delayer1207, and an adder1209. The multiplier1208may multiply decisions by error signals, which may be multiplied by a parameter. The multiplier1210may multiply feedback filter coefficients biby constants αi. The delayer1207may delay the outputs of the multiplier1210. The adder1209may add the outputs of the delayer1207and the outputs of the multiplier1208and may generate filter coefficients (e.g., feedback filter coefficients).

Referring toFIG. 12, the adaptation algorithm of the decoder (e.g., the TCM decoder), which may be combined with the equalizer (e.g., a DFE) illustrated inFIG. 12, may be the same, or substantially the same, as that as has been described with respect toFIG. 10A. With regard toFIG. 12, a first group of constants α1, α2, . . . , α12may have the same, or substantially the same, first value, which may correspond to the 0th traceback depth (see table 1), a second group of constants α13, α14, . . . , α24may have the same, or substantially the same, second value, which may correspond to the 1st traceback depth, a third group of constants α25, α26, . . . , α36may have the same, or substantially the same, third value, which may correspond to the 2nd traceback depth, . . . , and an N+1th group of, for example, 12 constants may have the same, or substantially the same, N+1 th value, which may correspond to the N-th traceback depth. Decisions, which may correspond to the 0th traceback depth, may be input to a first group of delay lines1225. . .1226, decisions, which may correspond to the 1st traceback depth may be input to a second group of delay lines1227. . .1228, . . . , and decisions, which may correspond to the N-th traceback depth may be input to an N+1 th group of delay lines1229. . .1230.

FIG. 13is an example of a construction diagram of the decoder (e.g., a TCM decoder) combined with the de-interleaver1216, according to an exemplary embodiment of the present invention.

FIG. 14is an example of a construction diagram of an equalizer (e.g., a DFE), which may be combined with a decoder (e.g., a trellis decoder). The equalizer may use the algorithm according to exemplary embodiments of the present invention and the “Stop-and-Go” algorithm to update filter coefficients (e.g., feedback filter coefficients).

In another exemplary embodiment of the present invention, the adaptation algorithm may be performed in a ‘Stop-and-Go’ mode as shown inFIG. 14. Referring toFIG. 14, a Stop-and-Go unit1424may generate a flag1425, which may disable updating of the equalizer coefficients if the decoder (e.g., TCM decoder) decisions are not reliable.

In the equalizer (e.g., the DFE) shown inFIG. 14, a filter (e.g., a feedback filter)1415may include a plurality of groups of cells, an adder1413, and a logic selector1431. Each group may be comprised of, for example, 12 cells. The adder1413may add the outputs of the cells and a logic selector1431may calculate constants (e.g., reliability constants).

The cells of the groups may receive values, which may be obtained by delaying (e.g., sequentially delaying) the decisions at the output of the decoder (e.g., TCM decoder)1416and error signals, which may be multiplied by a step-size parameter μ. Each of the cells of the groups may include an updater, which may update filters (e.g., feedback filters), and a multiplier1411, which may multiply filter coefficients (e.g., feedback filter coefficients) by input decisions.

An updater (e.g., a feedback filter updater) may include a multiplier1408, a multiplier1410, a delayer1407, and an adder1409. The multiplier1408may multiply decisions by error signals, which may be multiplied by a parameter. The multiplier1410may multiply filter coefficients (e.g., feedback filter coefficients) biby constants αi. The delayer1407may delay the outputs of the multiplier1410. The adder1409may add the outputs of the delayer1407and the outputs of the multiplier1408and may generate filter coefficients (e.g., feedback filter coefficients).

Referring toFIG. 14, the adaptation algorithm of the decoder (e.g., TCM decoder) may be combined with the equalizer (e.g., DFE) shown inFIG. 14may be the same, or substantially the same, as the algorithm as discussed above with regard toFIG. 12. A first group of constants α1, α2, . . . , α12may have the same, or substantially the same, first value, which may correspond to the 0th traceback depth (see table 1), a second group of constants α13, α14, . . . , α24may have the same, or substantially the same, second value, which may correspond to the 1st traceback depth, a third group of constants α25, α26, . . . , α36may have the same, or substantially the same, third value, which may correspond to the 2nd traceback depth, . . . , and an N+1th group of 12 constants may have the same, or substantially the same, N+1th value, which may correspond to the N-th traceback depth. Decisions, which may correspond to the 0th traceback depth, may be input to a group of, for example, delay lines1225. . .1226, decisions, which may correspond to the 1st traceback depth, may be input to a second group of, for example, delay lines1227. . .1228, . . . , and decisions which may correspond to the N-th traceback depth may be input to a group of, for example, delay lines1229. . .1230.

The SAG unit1426may receive decisions, may detect less reliable decisions, and may generate enable/disable signal. The SAG unit1426may disable adaptation if the decisions are less reliable, and may update equalizer coefficients if the decisions are more likely to be correct.

An example of a result of using the adaptation algorithm, according to the exemplary embodiments of the present invention, is illustrated inFIG. 15.

FIG. 15is a graph showing an example of a comparison of the output signal-to-noise ratio (SNR) versus the number of iterations for an equalizer (e.g., a DFE), which may be combined with a decoder (e.g., a trellis decoder).

As illustrated inFIG. 15, the operation of the equalizer (e.g., DFE) may be smoother and improved when using the algorithm according to exemplary embodiments of the present invention.

Various modifications may be made to circuits using the adaptation algorithm according to exemplary embodiments of the present invention. For example, decoder (e.g., TCM decoder) decisions, which may correspond to the depth N, may be used to generate error signals ek and drive the adaptation process. Any number of interleaved encoders may be used in examples shown inFIG. 12or14. For example, the number of encoders may be 8 or 16. Further, more efficient methods may be used to implement the algorithm shown in Equation 3 in hardware, which may employ shifters and/or adders instead of, or along with, multipliers.

Although exemplary embodiments of the present invention have been described with regard to voice, data, or video communications, it will be understood that exemplary embodiments of the present invention may be utilized in any suitable communications technique or combination thereof.

Although exemplary embodiments of the present invention have been described with regard to video ghosting and/or echoes, it will be understood that exemplary embodiments of the present invention may be utilized to reduce any form of fading and/or interference, as desired by one of ordinary skill in the art.

Although exemplary embodiments of the present invention have been described with regard to wireless modems and/or telephones, it will be understood that exemplary embodiments of the present invention may be utilized in any wireless or terrestrial communications system.

Although exemplary embodiments of the present invention have been described with regard to a trellis or viterbi decoder, it will be understood that any suitable decoder may be utilized as desired by one of ordinary skill in the art.

Although exemplary embodiments of the present invention have been described with regard to an 8-VSB trellis coded system for digital video broadcasting, it will be understood that exemplary embodiments of the present invention may be utilized in any suitable system for video, audio, and/or data systems.

Although exemplary embodiments of the present invention have been described with regard to an equalizer including twelve cells, it will be understood that any suitable number of cells may be utilized as desired by one of ordinary skill in the art.

As described above, according to exemplary embodiments of the present invention, the method of updating feedback filter coefficients using a DFE combined with a trellis decoder TCM may reduce the instability of the DFE, which may be due to the propagation of TCM decision errors, may improve the performance of the DFE combined with the trellis decoder TCM, and may enhance the performance of, for example, HDTV 8-VSB receivers.