On fractionally-spaced equalizer design for digital microwave radio channels

Johnson, C.R.; Lee, H.J.; LeBlanc, James; Endres, T.J.; Casas, R.A.; Tai, E.M.; Reznic, Z.; Meyer, Wolfram

doi:10.1109/acssc.1995.540558

Cited by 9 publications

(7 citation statements)

References 10 publications

(1 reference statement)

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“…Then, all the equalities above are true at time Further, let the above results be true at a time instant Since is independent of the scale factor , also does not depend on From (18), this implies that since by assumption. This immediately leads to the fact that from (15) and from (16), and it is easy to conclude that…”

Section: Appendix Proofs Of Resultsmentioning

confidence: 99%

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BEACON: an adaptive set-membership filtering technique with sparse updates

Nagaraj

Gollamudi

Kapoor³

et al. 1999

IEEE Trans. Signal Process.

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Abstract-This paper deals with adaptive solutions to the socalled set-membership filtering (SMF) problem. The SMF methodology involves designing filters by imposing a deterministic constraint on the output error sequence. A set-membership decision feedback equalizer (SM-DFE) for equalization of a communications channel is derived, and connections with the minimum mean square error (MMSE) DFE are established. Further, an adaptive solution to the general SMF problem via a novel optimal bounding ellipsoid (OBE) algorithm called BEACON is presented. This algorithm features sparse updating, wherein it uses about 5-10% of the data to update the parameter estimates without any loss in mean-squared error performance, in comparison with the conventional recursive least-squares (RLS) algorithm. It is shown that the BEACON algorithm can also be derived as a solution to a certain constrained least-squares problem. Simulation results are presented for various adaptive signal processing examples, including estimation of a real communication channel. Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. This improvement is a result of incorporating an explicit test to check if an update is needed at every time instant as well as an optimal datadependent assignment to the updating weights whenever an update is required.

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Section: Appendix Proofs Of Resultsmentioning

confidence: 99%

“…To illustrate this method, consider a microwave radio channel obtained from actual field measurements [16]. A symbolspaced equalizer is used and also the channel impulse response is truncated to 20 significant coefficients (i.e., ).…”

Section: ) For Eachmentioning

confidence: 99%

BEACON: an adaptive set-membership filtering technique with sparse updates

Nagaraj

Gollamudi

Kapoor³

et al. 1999

IEEE Trans. Signal Process.

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“…This technique, used with a preselected delay in [9] and the optimal delay in [12], is applied in this section to show how various channels affect the quality of the received signal. Figure 15a shows the channel model estimated using the Gooch and Harp technique on a limited, contiguous record of data from a 64-QAM digital microwave radio.…”

Section: Data Setmentioning

confidence: 99%

Fractionally spaced equalizers

Treichler¹,

Fijalkow²,

Johnson³

1996

IEEE Signal Process. Mag.

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198

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“…The two adaptive approaches are twice as complex as the non-adaptive approaches also shown in this paper. This results of the additional update equation (8) which has similar complexity as the convolution and thus depends on the filter length.…”

Section: Equalizationmentioning

confidence: 99%

“…Similarly to the first nonlinear equalizer design, the calculation of these equalizer coefficients is done by computing the first order kernel of the equalizer with the conventional equations given in [8] or Section V and finding the optimal second order equalizer for the determined linear equalizer coefficients. Consequently, the third and fourth order kernel of the cascade are given as [9], [10] …”

Section: Equalizationmentioning

confidence: 99%

Nonlinear Equalization for Frame-Differential IR-UWB Receivers

Krall

Witrisal

Koeppl

et al.

2005 IEEE International Conference on Ultra-Wideband

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Abstract-This paper shows equalization approaches for highdata-rate transmitted-reference (TR) IR-UWB systems employing an autocorrelation receiver front-end. Using a maximum likelihood sequence detector (MLSD) with decision feedback (in the back-end of the TR-receiver), a significant improvement of the receiver performance is possible. To avoid the high complexity of the MLSD detector, alternative equalizer structures are evaluated. If the parameters of the channel are not known a priori, an equalizer has to be adapted during the transmission of training data. Such an adaptive equalizer is presented as a reference. Furthermore, we study the design of Minimum Mean Square Error (MMSE) equalizers assuming knowledge of the channel. A linear MMSE equalizer is designed using the linear and nonlinear channel coefficients. Then the concept of the linear equalizer is extended to a nonlinear Volterra equalizer which further improves the performance of the IR-UWB receiver structure. All proposed methods are discussed and the effects are shown with computer simulations.

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On fractionally-spaced equalizer design for digital microwave radio channels

Cited by 9 publications

References 10 publications

BEACON: an adaptive set-membership filtering technique with sparse updates

BEACON: an adaptive set-membership filtering technique with sparse updates

Fractionally spaced equalizers

Nonlinear Equalization for Frame-Differential IR-UWB Receivers

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