Decision feedback equalizer design for insensitivity to decision delay

Hillery, W.J.; Zoltowski, M.D.; Fimoff, M.

doi:10.1109/icassp.2003.1202690

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…More specifically, because it can also be easily verified that for every , there are of the searching points on the "branches," and one searching point on the "trunk," corresponding to as only then . Since the "branches" always lie above the "trunk," this indicates that the approach described in [2] cannot guarantee convergence to the optimum value, and the performance loss may happen when , where is the optimum defined in (6). The exact value of the performance loss depends on the specific channel.…”

Section: Some Discussionmentioning

confidence: 99%

“…Thus, (or the "trunk") is a strictly nonincreasing function of , and then can be redefined as the minimum that satisfies (6) It is clear from Fig. 1 that, in this example, we have and since the "trunk" curve almost flattens out after .…”

Section: From the Theorem 3 In [4] We Havementioning

confidence: 95%

“…Chen et al pointed out in [5] that the DFE should have and . In a more recent paper [6], Hillery et al further argued that, when is at least as long as the channel span, the DFE is insensitive to the decision delay for a fairly wide band. What the exact value of an appropriate should be, however, still remains a question.…”

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confidence: 99%

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A self-structured adaptive decision feedback equalizer

Gong

Cowan

2006

IEEE Signal Process. Lett.

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Abstract-In a decision feedback equalizer (DFE), the structural parameters, including the decision delay, the feedforward filter (FFF), and feedback filter (FBF) lengths, must be carefully chosen, as they greatly influence the performance. Although the FBF length can be set as the channel memory, there is no closed-form expression for the FFF length and decision delay. In this letter, first we analytically show that the two-dimensional search for the optimum FFF length and decision delay can be simplified to a one-dimensional search and then describe a new adaptive DFE where the optimum structural parameters can be self-adapted.Index Terms-Adaptive algorithm, decision delay, decision feedback equalizer (DFE), tap-length. I. PROBLEM STATEMENTST HE typical structure of a decision feedback equalizer (DFE) consists of a feedforward filter (FFF), a feedback filter (FBF), and a decision device. Although an ideal DFE generally has infinite length [1], most designs use finite length filters because of simplicity and robustness. This brings up the problem of how to choose the structural parameters, including the FFF tap-length , the decision delay , and the FBF tap-length , as they have great influence on the performance. Note that the decision delay determines to which transmitted symbol the detected symbol corresponds. To be specific, on the one hand, when and are fixed, there exists an optimum that minimizes the minimum mean-square error (MMSE); on the other hand, for a given , the MMSE is always a nonincreasing function of or , but too long a or not only unnecessarily increases the complexity with little MMSE improvement but also increases the adaptation noise when the adaptive algorithm is applied. Therefore, for all possible choices of , , and , there must exist a group of optimum values of them that correspond to the best MMSE performance. The purpose of this letter is, therefore, to search for the optimum structural parameters that normally vary with different channels.Some designs, by comparison, intend to reach a preset target MMSE with the simplest complexity, such as the smallest taplength. However, how to choose the target MMSE, thereafter, becomes another issue that is different from that of this letter, where we want the MMSE as small as possible. In some other approaches, the algorithm fixes the tap-length based on a tolerable complexity and finds the corresponding optimum . For example, in [2], fixing the total number of taps " ," Al-Dhahir et al. described a method to search for the optimum values of and that minimize the MMSE. Such an approach, however, has two disadvantages: First, it did not point out how to choose an appropriate value of , which, similar to that for the linear equalizer [3], should balance the complexity and performance and thus generally varies with the channel. Thus, the resulting structural parameters are only "locally" optimum for one particular choice of . Second, although the assumption of fixing attempts to place a constraint on the complexity, two DFEs with the same may vary...

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Section: Some Discussionmentioning

confidence: 99%

Section: From the Theorem 3 In [4] We Havementioning

confidence: 95%

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A self-structured adaptive decision feedback equalizer

Gong

Cowan

2006

IEEE Signal Process. Lett.

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“…Although an ideal MMSE equalizer generally has infinite tap length [1], most designs use finite length filters because of simplicity and robustness. This brings up the problem of how to choose the tap length and decision delay which significantly affect the performance: on the one hand, though the MMSE is a monotonic non-increasing function of the tap-length, "too" long an equalizer not only unnecessarily increases the complexity but also introduces more adaptation noise; on the other hand, for a given filter length, the MMSE is generally a concave function of the decision delay [2]. Therefore, there exist optimum values of the tap-length and decision delay that best balance the steady-state performance and complexity.…”

Section: Introductionmentioning

confidence: 99%

Flexible MMSE DFE using length and LAG adaptation

Gong

Cowan

2005

IEEE/SP 13th Workshop on Statistical Signal Processing, 2005

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This paper investigates how to choose the optimum taplength and decision delay for the decision feedback equalizer (DFE). Although the feedback filter length can be set as the channel memory, there is no closed-form expression for the feedforward filter length and decision delay. In this paper, first we analytically show that the two dimensional search for the optimum feedforward filter length and decision delay can be simplified to a one dimensional search, and then describe a new adaptive DFE where the optimum structural parameters can be self-adapted.

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“…The value of the time lag must be carefully chosen as it greatly influences the performance: First, there exists a minimum MMSE with respect to the time lag, and the MMSE for different time lag can vary significantly especially when the tap-length is short [2]. Second, when the tap-length is large, the equalizer may not be sensitive to some choices of the time lag, as was shown in [3] that adjusting the number of taps in the decision feedback equalizer (DFE) can make it robust to variation of the decision delay. Under such a situation, plotting the MMSE versus the time lag will show a graph with a "flat line" around the time lag that minimizes the MMSE.…”

Section: Introductionmentioning

confidence: 99%