WO2003071679A1 - Filtrage adaptatif a compensation de polarisation en courant continu - Google Patents

Filtrage adaptatif a compensation de polarisation en courant continu Download PDF

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Publication number
WO2003071679A1
WO2003071679A1 PCT/US2003/005233 US0305233W WO03071679A1 WO 2003071679 A1 WO2003071679 A1 WO 2003071679A1 US 0305233 W US0305233 W US 0305233W WO 03071679 A1 WO03071679 A1 WO 03071679A1
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WIPO (PCT)
Prior art keywords
samples
filter
parameter
filter coefficients
algorithm
Prior art date
Application number
PCT/US2003/005233
Other languages
English (en)
Inventor
Ivan Jesus Fernandez-Corbaton
Srikant Jayaraman
John Smee
Original Assignee
Qualcomm Incorporated
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US10/081,857 external-priority patent/US20030156635A1/en
Priority claimed from US10/115,210 external-priority patent/US20030185292A1/en
Application filed by Qualcomm Incorporated filed Critical Qualcomm Incorporated
Priority to AU2003213180A priority Critical patent/AU2003213180A1/en
Publication of WO2003071679A1 publication Critical patent/WO2003071679A1/fr

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Classifications

    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H21/00—Adaptive networks
    • H03H21/0012—Digital adaptive filters

Definitions

  • the present invention relates generally to communications systems, and more specifically, to systems and techniques for adaptive filtering with DC bias compensation.
  • Communications systems are used for transmission of information from one device to another.
  • the devices included in the communications systems typically have either a transmitter, a receiver, or both.
  • the function of the transmitter is to encode information and modulate the encoded information into an analog signal suitable for transmission over a communications channel.
  • the function of the receiver is to detect the analog signal in the presence of noise, demodulate the detected analog signal to recover the encoded information, and decode the information.
  • the receiver In the process of demodulating the analog signal, the receiver typically performs an analog to digital conversion to obtain digital samples of the detected analog signal.
  • the device used for this purpose is typically an analog- to-digital converter (ADC).
  • ADC analog- to-digital converter
  • this device operates by comparing the input voltage of the detected analog signal to a fixed reference voltage and quantizing the difference into a digital sample with a specified number of bits.
  • the fixed reference voltage can be interpreted as the "zero" of the ADC, or equivalently, as the input signal voltage which translates to a "zero" for the digital sample.
  • the reference voltage should always be constant. However, due to various practical factors like noise, tolerance of the ADC components, etc., the reference voltage is typically not fixed.
  • this bias causes a narrow noise peak near the zero frequency (DC) of the signal spectrum.
  • some receiver configurations may introduce a bias in the detected analog signal even before the ADC.
  • the DC bias may have a particularly undesirable effect. Since the adaptive filter shapes its frequency response based on the signal and noise power spectral densities, a narrow noise peak near the zero frequency constrains the adaptive filter to shape its response accordingly. This constraint results in a performance loss because the adaptive filter has fewer degrees of freedom with which to synthesize an optimal response at other frequencies.
  • a method of filtering a plurality of samples includes adapting a plurality of filter coefficients, and filtering a plurality of samples by applying one of the filter coefficients to a parameter, applying each remaining filter coefficient to one of the samples, and combining the parameter and the samples, wherein the adaptation of the filter coefficients is a function of the combined parameter and samples.
  • a receiver in another aspect of the present invention, includes an analog-to-digital converter configured to sample an analog signal to produce a plurality of samples, and a filter having a coefficient generator configured to adapt a plurality of filter coefficients, the filter being configured to apply one of the filter coefficients to a parameter, apply each of the remaining filter coefficients to one of the samples, and combine the parameter and the samples, the adaptation of the filter coefficients being a function of the combined parameter and samples.
  • a filter in yet another aspect of the present invention, includes a delay element configured to serially receive a plurality of samples, a coefficient generator configured to adapt a plurality of coefficients, a first multiplier configured to multiply said one of the filter coefficients with the parameter, a second multiplier configured to multiply each remaining filter coefficient with one of the samples from the delay element, and an adder configured to sum the parameter and the samples, wherein the adaptation of the filter coefficients is a function of the summed parameter and samples.
  • computer-readable media embodying a program of instructions executable by a computer program performs a method of adapting filter coefficients including adapting a plurality of filter coefficients, and filtering a plurality of samples by applying one of the filter coefficients to a parameter, applying each remaining filter coefficient to one of the samples, and combining the parameter and the samples, wherein the adaptation of the filter coefficients is a function of the combined parameter and samples.
  • a filter includes means for adapting a plurality of filter coefficients, and means for filtering a plurality of samples by applying one of the filter coefficients to a parameter, applying each of the remaining filter coefficients to one of the samples and combining the parameter and the samples, wherein the adaptation of the filter coefficients is a function of the combined parameter and samples.
  • FIG. 1 is a functional block diagram of a communications device employing an exemplary receiver
  • FIG. 2 is a functional block diagram of an exemplary adaptive filter which can be used with the receiver of FIG. 1 ;
  • FIG. 3 is functional block diagram of a communications device employing an exemplary receiver with a DC notch filter
  • FIG. 4 is a functional block diagram of a communications device employing an exemplary receiver arrangement capable of supporting multiple antennas.
  • an adaptive filtering process can be performed which corrects for DC bias. This can be achieved by adapting a number of filter coefficients during the transmission of a known sequence from a remote source. One of the filter coefficients can then be applied to a parameter to produce a weighted parameter, and the remaining filter coefficients can be applied to the digital samples to produce a number of weighted digital samples. The weighted parameter can be combined with the weighted digital samples to produce estimates of the transmitted symbols.
  • the adaptation of the filter coefficients can be performed using any classical least squares algorithm including a "least mean square” (LMS) algorithm, a “recursive least squares” algorithm (RLS), a direct least squares matrix inversion of an estimated autocorrelation matrix, or any other algorithm known in the art.
  • LMS least mean square
  • RLS recursive least squares algorithm
  • a direct least squares matrix inversion of an estimated autocorrelation matrix or any other algorithm known in the art.
  • FIG. 1 is a functional block diagram of a communications device employing an exemplary receiver.
  • the communications device 100 includes an antenna 102 configured to receive a wireless transmission.
  • the communications device 100 can be configured to receive a transmission by way of cable, fiber optic link, digital subscriber line, or any other communications medium known in the art.
  • the transmission received by the antenna 102 can be provided to a receiver 104.
  • the receiver can be based on a heterodyne complex (l-Q) architecture.
  • l-Q heterodyne complex
  • the receiver 104 may have an analog front end (AFE) 106 which amplifies, filters and downconverts the transmission to an analog complex baseband signal.
  • the analog baseband signal from the AFE 106 can be provided to an ADC 108 to produce digital complex baseband samples.
  • the digital baseband samples from the ADC 108 can then be provided to an adaptive filter 110.
  • the adaptive filter 110 can be used to compensate for ISI which occurs as a result of the spreading of a transmitted symbol pulse due to the dispersive nature of the communications medium which results in an overlap of adjacent symbol pulses.
  • the adaptive filter 110 may be implemented with a transversal filter, such as a "finite impulse response" (FIR) filter.
  • FIR finite impulse response
  • DFE decision feedback equalizer
  • the adaptive filter 110 may be implemented with a multiple-tap delay line.
  • the output of the taps can be weighted and summed to generate a "soft estimate" of the transmitted symbol.
  • the tap coefficients can be adapted to maximize the Signal to Interference and Noise Ratio (SINR) of the symbols estimates at the filter's output.
  • the adaptive filter 110 can use a prescribed algorithm, such as a "least mean squares" (LMS) algorithm, a "recursive least squares” (RLS) to estimate the tap coefficients, or any other algorithm known in the art.
  • LMS low mean squares
  • RLS recursive least squares
  • the "soft estimate” generated by the adaptive filter 110 can be used by a decision making device such as a slicer or a decoder (not shown).
  • the exemplary communications device will be described from hereon with the assumption that the received transmission is sampled by the ADC 108 at a rate of one sample per symbol period T, and that the adaptive filter 110 is an T-spaced filter. These assumptions are made for illustrative purposes only, and those skilled in the art will readily appreciate that the inventive concepts described throughout can be extended to other sampling rates and filter tap spacings.
  • the digital baseband samples from the ADC 108 can be represented by a stream of digital samples x(k) that contain the transmitted symbols y(k), ISI and noise.
  • This stream ⁇ x(k) ⁇ can be filtered by the adaptive filter 110 to produce estimates y(k) of the transmitted symbols.
  • the symbol estimates y(k) can be expressed as:
  • H is a column vector of length N containing the filter coefficients
  • H denotes the Hermitian operation
  • X(k) is a column vector of length N containing N consecutive digital samples from the ADC 108.
  • the column vector H for the filter coefficients can be represented as:
  • the standard criterion for optimizing the filter coefficients H of the adaptive filter 110 is by the mean square error (MSE) of the symbol estimates which can be expressed as:
  • Optimal performance in terms of maximizing signal-to-interference-and-noise ratio is generally achieved by minimizing the MSE. This can be accomplished by adapting the filter coefficients with a least square algorithm, or other known algorithm, using the pilot sequence in the transmission. Since the pilot sequence is known, a priori, the MSE can be minimized using the soft symbol estimates generated by the adaptive filter 110.
  • H(k + ⁇ ) H(k) + ⁇ X(k)e(k) H , (5)
  • Equation (5) represents an adaptation step of the typical LMS algorithm where ⁇ is a gain constant (or adaptation constant) that regulates the speed and stability of adaptation and the steady state MSE performance of the filter.
  • ⁇ is a gain constant (or adaptation constant) that regulates the speed and stability of adaptation and the steady state MSE performance of the filter.
  • the LMS algorithm can be implemented in a practical system without squaring, averaging, or differentiation.
  • a bias correction scheme can be implemented by the adaptive filter 110 to compensate for DC bias introduced by the AFE 106, the ADC 108 and/or any other receiver component.
  • the bias correction scheme can be implemented with a modified "minimum mean square error" (MMSE) computation from which a LMS algorithm can be derived.
  • MMSE minimum mean square error
  • This approach to bias correction may provide certain advantages. By way of example, it does not require larger wordlengths of the baseband samples to remove a bias smaller than 1 LSB of the baseband samples after conversion from the analog domain to digital and it has the ability to track time varying bias.
  • a feedback circuit employing an outer correction loop 112 may be used to control the clipping of the digital baseband samples by the ADC 108 should the DC bias become excessive. Since the ADC 108 uses a finite number of bits to represent the analog baseband signal, clipping may occur if the analog baseband signal falls outside the numerical range that can be represented with the specified number of bits. If the digital baseband samples are clipped, information may be lost irretrievably, thus reducing the performance of the adaptive filter.
  • the outer correction loop 112 allows the degree of clipping to be controlled to an acceptable level.
  • the design and implementation of the outer correction loop 112 is well known.
  • the outer correction loop 112 can be designed to monitor the DC bias at the output of the ADC 108.
  • the outer correction loop 112 sends a feedback a signal to the ADC 108 to compensate for the DC bias if its measured absolute value exceeds a predetermined threshold.
  • the DC bias can be reduced by adjusting the fixed reference voltage of the ADC 108.
  • the adaptive filter 110 can then be used to remove any residual DC bias whose absolute value at the output of the ADC 108 does not exceed the preset threshold.
  • the outer correction loop 112 does not need to be as stringent as those used in the past, thus allowing a superior design that achieves higher performance as well as a cheaper and more flexible implementation.
  • the DC bias in the digital baseband samples can be modeled by adding a fixed complex number to the digital baseband samples from an ideal bias-free ADC as follows:
  • x(k) denotes the digital baseband samples from an ideal bias-free ADC
  • b represents the DC bias
  • x'(k) denotes the actual digital baseband samples generated by the ADC.
  • H(k + ⁇ ) H(k) + X'(k)e(k) * , (8)
  • ⁇ is a new coefficient to optimize
  • ⁇ is a fixed parameter whose value is constant and not adapted
  • the subscript * denotes complex conjugation.
  • the fixed parameter should be chosen to be similar in power level to the digital baseband samples.
  • the choice of ⁇ is not critical and may take on any value depending on the particular application and overall design constraints.
  • a modified MSE computation can be represented as:
  • a modified LMS algorithm using a stochastic steepest descent search algorithm can then be employed to adapt the filter coefficients during the pilot sequence from the following algorithm derived from equation (9):
  • Z(k) [x'(k) ] H ; e(k) is the error, which is the difference between y(k) (known a priori) and y(k); and ⁇ is the gain constant (or an adaptation constant).
  • a coefficient generator 202 can be used to compute and update the filter coefficients, including the new coefficient ⁇ , during transmission of the pilot sequence, using the modified LMS algorithm of equation (11).
  • a tapped delay line 204 can employ delay elements, such as shift registers, arranged in series to temporarily store the serial digital baseband samples from the ADC 108 (see FIG. 1).
  • the generation of the soft symbol estimates y(k) entails multiplying the output of each delay element with a filter coefficient using multipliers 206 (one for each delay element output) and multiplying the fixed parameter ⁇ with the complex conjugate of the new adapted coefficient ⁇ with a multiplier 208.
  • the outputs of the multipliers 206 and the multiplier 208 can then be summed with an adder 210 to produce the soft symbol estimates y(k).
  • the output of each delay element and the soft symbol estimates y(k) are fed back to the coefficient generator 202.
  • a locally generated pilot sequence y(k) can be provided to the coefficient generator 202 from a pilot sequence generator (not shown).
  • the gain constant ⁇ and the fixed parameter ⁇ can be provided to the coefficient generator 202 from a processor, memory, or any other device. From these inputs, the modified LMS algorithm can be used to adapt the filter coefficients during the pilot sequence of the transmission.
  • FIG. 3 is a functional block diagram of a communications device employing an exemplary receiver with a DC notch filter.
  • a DC notch filter 302 may be placed at the input to the adaptive filter 110 to facilitate the convergence of the modified LMS algorithm by reducing large values of DC bias that might otherwise slow down the convergence of the filter coefficients due to an increase in the eigenvalue spread of the signal autocorrelation matrix.
  • the DC notch filter 302 is shown at the input to the adaptive filter 110.
  • the DC notch filter 302 could be placed at any other point in the receiver path if said point is before the adaptive filter.
  • a DC notch filter 302 can be implemented, either digitally or with analog components.
  • the DC notch filter 302 may be implemented as an analog filter in the AFE 106 or as a digital filter after the ADC 108. It should be noted that the use of the DC notch filter 302 by itself, or together with an outer correction loop 112, may not completely remove the DC bias because any realizable filter may have residual bias at its output. However, when used in combination with an adaptive filter employing the modified LMS algorithm of equation (11), substantially no loss in receiver performance should be experienced due to DC bias.
  • FIG. 4 is a functional block diagram of a communications device with an exemplary receiver architecture supporting multiple antennas.
  • multiple antennas 402, 404, and 406 can be arranged for diversity reception in order to mitigate the effects of multipath interference and improve overall system throughput.
  • Each antenna has associated with it a respective AFE 408, 410, and 412, an ADC 414, 416, and 418, and an adaptive filter 420, 422, and 424.
  • each ADC 414, 416, and 418 can be respectively provided with an outer correction loop 426, 428, 430 to control clipping caused by excessive DC bias.
  • Each of the ADCs 414, 416, and 418 may also have a DC notch filter 432, 434, and 436 positioned at its respective output.
  • the DC notch filters can be located anywhere in the receive path before the adaptive filter and can be implemented as an analog or digital filter.
  • A is the number of antennas
  • Each row of the matrix M consists of all zeros except for a 1 in the appropriate position to select the DC bias corresponding to that antenna out of the column vector for the DC bias B.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
  • a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • a software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
  • An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium.
  • the storage medium may be integral to the processor.
  • the processor and the storage medium may reside in an ASIC.
  • the ASIC may reside in a user terminal.
  • the processor and the storage medium may reside as discrete components in a user terminal.

Abstract

Cette invention porte sur des systèmes et des techniques permettant de filtrer des échantillons numériques. Selon cette invention, on adapte un certain nombre de coefficients de filtre et on filtre les échantillons numériques en appliquant un de ces coefficients de filtre à un paramètre, en appliquant chaque coefficient de filtre restant à l'un des échantillons et en combinant le paramètre avec les échantillons. L'adaptation des coefficients de filtre est une fonction de la combinaison du paramètre et des échantillons numériques.
PCT/US2003/005233 2002-02-20 2003-02-19 Filtrage adaptatif a compensation de polarisation en courant continu WO2003071679A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2003213180A AU2003213180A1 (en) 2002-02-20 2003-02-19 Adaptive filtering with dc bias compensation

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US10/081,857 US20030156635A1 (en) 2002-02-20 2002-02-20 Adaptive filtering with DC bias compensation
US10/081,857 2002-02-20
US10/115,210 US20030185292A1 (en) 2002-04-02 2002-04-02 Adaptive filtering with DC bias compensation
US10/115,210 2002-04-02

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113676157A (zh) * 2021-08-11 2021-11-19 武汉理工大学 一种抵消直流偏置的自适应滤波算法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0725480A1 (fr) * 1995-02-01 1996-08-07 Nec Corporation Filtre à commande adaptatif
US5615233A (en) * 1992-07-22 1997-03-25 Motorola, Inc. Method for channel estimation using individual adaptation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5615233A (en) * 1992-07-22 1997-03-25 Motorola, Inc. Method for channel estimation using individual adaptation
EP0725480A1 (fr) * 1995-02-01 1996-08-07 Nec Corporation Filtre à commande adaptatif

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113676157A (zh) * 2021-08-11 2021-11-19 武汉理工大学 一种抵消直流偏置的自适应滤波算法

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