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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMSI: REGULAR PAPERS, VOL. 54, NO. 9, SEPTEMBER 2007 1891

Adaptive Noise Cancellation Techniques inSigmaDelta Analog-to-Digital Converters

Bahar Jalali-Farahani, Member, IEEE, and Mohammed Ismail, Fellow, IEEE

AbstractAdaptive noise cancellation (ANC) techniques thatextract a desired signal from background noise have manyapplications in different engineering disciplines. In ANC, thecorrupted signal is passed through a filter that tends to sup-press the noise while leaving the original signal unchanged. Thispaper demonstrates that the adaptive noise cancellation tech-nique can be embedded in the digital signal postprocessing of asigmadelta analog-to-digital converter and effectively reducesthe quantization noise as well as the thermal noise at the outputof the converter. The combination of ANC and the noise-shapingtechnique enable high-resolution analog-to-digital conversion inwideband applications where noise shaping alone cannot provideenough suppression of quantization noise due to the low oversam-

pling ratio.

Index TermsAdaptive, analog-to-digital converter, noise can-cellation, sigmadelta modulator.

I. INTRODUCTION

CONTINUOUS development in broadband wireless sys-

tems forces analog-to-digital converters to provide more

resolution in larger signal bandwidths. Although Nyquist rate

analog-to-digital converters can operate in high speeds, they are

limited in resolution, and higher speed comes with a significant

increase in power consumption. Oversampling sigmadelta

modulators are a better choice for high resolution in low tomedium speeds. For broadband signals, a large oversampling

ratio cannot be chosen; therefore, sigmadelta converters do not

gain much benefit from noise shaping. Besides a lower over-

sampling ratio, they also suffer from analog circuit nonidealities

which become more prominent in deep submicron technology.

Different techniques have been proposed to improve the perfor-

mance of sigmadelta modulators for wideband applications.

Distributing zeros of the noise-transfer function across the

signal bandwidth is proposed in [1] to achieve 8-bit resolution

in the 40-MHz bandwidth. A Chebyshev-based loop filter de-

sign is used in [2] to provide more effective noise shaping in a

low oversampling ratio than a conventional modulator. Higherresolution in sigmadelta converters can be achieved by using

higher order multistage noise shaping (MASH) stages without

sacrificing stability; however, nonidealities in the analog cir-

cuits cause the incomplete cancellation of the quantization

noise at the output of a modulator and degrade its performance.

Manuscript received October 27, 2005; revised February 5, 2007. This worksupported by the Semiconductor Research Corporation (SRC).

B. Jalali-Farahani is with the Department of Electrical Engineering, ArizonaState University, Tempe, AZ 85287-1203 USA (e-mail: [email protected]).

M. Ismail is with the Analog Very Large Scale Integration Lab, Departmentof Electrical and Computer Engineering, The Ohio State University, Columbus,OH 43210 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/TCSI.2007.904655

Fig. 1. Block diagram adaptive noise cancellation (ANC) technique.

This paper explores a new approach to enhance the signal-to-

noise ratio of a sigmadelta modulator for broadband applica-

tions by using an adaptive noise cancellation (ANC) technique.

An ANC that extracts the desired signal from the corrupted

noise has found many applications in different engineering dis-

ciplines, such as the suppression of periodic interference in tele-

phone lines [3], acoustic noise cancellation [4], adaptive line

enhancement [5][7], and automatic speech recognition [8] to

mention a few. The idea is to use an adaptive filter that exploits

the spectral difference between the desired signal and the noise

to separate them from each other. In case of sigmadelta ADC,

we used ANC to extract the narrowband signal from the wide-

band quantization noise. Since ANC is added to the output of a

sigmadelta modulator, all of the required signal processing is

performed in the digital domain and no complexity is added to

the modulator itself.

This paper is organized as follows. Section II describes the

principles of the ANC technique. The application of ANC in

sigmadelta modulators is proposed in Section III. The transfer

function of the ANC filter in presence of the high-passed quan-

tization noise is derived and the amount of improvement in the

signal-to-noise ratio is formulated. Section IV presents the sim-

ulation results to apply this technique to different sigmadeltamodulators. Practical considerations regarding the linearity of

the adaptive filter and the digital complexity associated with the

ANC block are presented in Section V. This paper concludes in

Section VI.

II. PRINCIPLES OF ANC TECHNIQUE

Fig. 1 shows the block diagram of the ANC system. An input

to the ANC block is the desired signal corrupted with wideband

noise, defined as

(1)

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1892 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMSI: REGULAR PAPERS, VOL. 54, NO. 9, SEPTEMBER 2007

ANC separates the two components of this signal by using an

adaptive filter. A delayed version of the input signal is passed

through the adaptive filter to generate the error signal given by

(2)

Using (1) and (2), the error signal can be written as

(3)

The least mean-squared algorithm (LMS) is used to tune the

coefficient of adaptive filter so that the error signal in (3) is min-

imized in the mean-squared sense. The mean-squared value of

the error is calculated by taking expectation from both sides of

(3)

(4)

If the adaptive filter is implemented by a finite-impulse-re-sponse (FIR) filter of order , the output of the filter can be

written as

(5)

It can be expressed in terms of signal and noise components by

(6)

If noise is a wideband random signal and the delay line is

long enough, different delayed samples of noise are completely

uncorrelated. Therefore, the current sample of the filter output

, expressed by (6), is also uncorrelated with the current

sample of noise signal which makes the third term in (4)

equal to zero. The second term in (4) is constant and is equal to

the noise variance . Therefore, minimizing the mean-squared

error in (4) forces the output sample to be a close estimate

of the desired signal .

Therefore, filter output in the ANC system provides the best

estimate of the desired narrowband signal in the mean-squared

sense, provided that noise is wideband compared to the signal

and has uncorrelated samples. In the next section, this problem

is formulated for the case where is the output of ath-order sigmadelta modulator and is composed of the

desired signal and the highpass-filtered quantization noise.

III. APPLICATION OF ANC TO SIGMADELTA ADCS

The adaptive noise cancellation technique shown in Fig. 1

can be used to improve the signal-to-noise ratio (SNR) at the

output of a sigmadelta modulator without any modification to

the analog circuit [9]. ANC relaxes the requirements on the fol-

lowing lowpass filter, which usually needs to be implemented

by a long FIR filter and is based on the application; it may even

remove the need for such filtering completely. The biggest ad-

vantage of ANC is its adaptability. Contrary to the fixed coef-ficient lowpass filters, ANC adapts its coefficients based on its

Fig. 2. Block diagram of a sigmadelta ADC including ANC.

input signal. Fig. 2 shows how ANC can be a part of digital post-

processing of a sigmadelta modulator.

In this section, the transfer function of the system shown in

Fig. 1 is derived at steady state with the assumption that the

coefficients of the adaptive filter have been already converged

to their optimum values given by the Wiener solution. All of

the signals are as indicated in Fig. 1. Under this assumption,

the ANC block is a linear discrete-time system with the transfer

function defined as

(7)

where and are the -transforms of the filter output

and desired signal, respectively. An input to the ANC block is

composed of a desired sinusoidal signal corrupted by the high-

pass filter quantization noise

(8)

The filter output can be expressed in terms of an input signal

and filter coefficients as

(9)

Since the input to the filter is the delayed version of the

desired signal, (9) results in

(10)

Using (7) and (10), the transfer function of the ANC system is

given by

(11)

where is the th coefficient of the filter and is the delay line

shown in Fig. 1.

It is shown in the Appendix that the transfer function in (11)

can be written in terms of modulator parameters: filter order

( ), input signal parameters amplitude ( ), and frequency ( ),

ANC block delay ( ), and adaptive filter order ( ). Equation

(A.25) is repeated here

(12)

where , , , and are defined in (A.26), (A.27), (A.12),and (A.20), respectively.

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JALALI-FARAHANI AND ISMAIL: ANC TECHNIQUES IN SIGMADELTA ADCS 1893

As explained in the Appendix, for large filter orders, ANC

almost has a unity transfer function for the sinusoidal input

(13)

However, wideband noise is attenuated by the factor given by(A.34)

(14)

which results in an improvement in SNR given by (A.37)

(15)

From (15), it can be seen that SNR improvement caused

by the ANC block is a function of three design parameters:

sigmadelta modulator order , signal bandwidth , and

ANC adaptive filter order . The effects of these parameters on

SNR are depicted in Fig. 3 and the following observations are

made.

SNR improvement depends on the adaptive filter order. In

fact, (12) suggests that the ANC block is a bandpass filter

with a center frequency equal to the input frequency .

The quality factor of this filter is increasedas the filter order

is increased which results in a better separation of signaland noise and, therefore, more improvement in the SNR.

SNR improvement obtained by the ANC block is almost

independent of the order of the sigmadelta modulator.

The ANC block is more effective when the oversampling

ratio is low. In this case, there is a significant amount of

inband quantization noise that can be effectively removed

by the ANC block.

IV. SIMULATION RESULTS

This section provides simulation results to support the con-

clusions drawn in Section III and the Appendix. It also shows

different aspects of using an adaptive noise cancellation tech-

nique. A fourth-order sigmadelta modulator is considered in

this section which is digitizing GSM as well as WLAN signals

in a multistandard transceiver. Fig. 4 shows a block diagram of

the sigmadelta ADC designed for this multistandard receiver.

All of the blocks are used for the GSM system. In the WLAN

mode, only the blocks with solid-line borders are used. There-

fore, the fourth-order modulator with 6-bit quantizer, five stages

of comb filter, a 10th-order halfband filter, and a 25th-order FIR

filter are used for GSM mode. Only the first stage of the modu-

lator, three stages of the comb filter and the 25th-order FIR filter

are used for the WLAN mode. Specifications for decimation and

lowpass filters are decided based on the system requirement fora direct homodyne receiver as explained in [10].

Fig. 3. SNR improvement as predicted by (15). Results are plotted for a full-

scalesinusoidal input signal, second-order sigmadelta modulator with O S R = 8 and adaptive filter order = 4 0 . The plots show the SNR improvement as afunction of (a) adaptive filter order with constant N and ! , (b) sigmadeltamodulator order with constant L and ! , and (c) oversampling ratio with con-stant N and L .

Fig. 4. Block diagram of the two-mode sigmadelta ADC used forGSM/WLAN standards.

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Fig. 5. Same as Fig. 4, but with an ANC block instead of a fixed-coefficient

FIR filter.

Fig. 6. Output spectrum of the sigmadelta ADC designed for the GSM stan-dard with and without using the ANC block.

Fig. 4 is modified by using an adaptive filter in an ANC con-

figuration as shown in Fig. 5. Simulation results provided in this

section are based on these two block diagrams. The normalizedleast mean square (NLMS) algorithm is used in ANC for up-

dating the coefficients.

A. Adaptive FIR Filter for GSM

The spectrum of an output signal in GSM mode is depicted in

Fig. 6 for the sigmadelta ADC with a conventional FIR filter

(i.e., Fig. 4) as well as ANC (i.e., Fig. 5). As seen in this figure,

ANC provides more attenuation of quantization noise inside the

signal bandwidth; however, its performance degrades in higher

frequencies. Overall SNR improvement is around 5 dB as also

predicted by (15). More significant improvements are achieved

by ANC when the oversampling ratio is low which is the case forthe WLAN modulator and is considered next. Filter coefficients

are converged after 10 000 iterations. Fig. 7 illustrates the con-

vergence of the first six coefficients of the 25th-order FIR filter.

B. Adaptive FIR Filter for WLAN

As predicted by (15) and Fig. 3, ANC results in significant

SNR improvement when the oversampling ratio is low. The

amount of inband quantization noise is significant for low over-

sampling ratios and can be effectively reduced by the ANC

block. Fig. 8 shows the results for the WLAN system where an

oversampling ratio is only 8. The SNR is improved from 69 dB

to 78 dB using the ANC block. The amount of SNR improve-ment predicted by (15) for and is 13 dB.

Fig. 7. Iteration of the adaptive filter coefficients in the ANC block.

Fig.8. Output spectrum of the sigmadelta ADCdesigned for the WLAN stan-dard with and without using an ANC block.

C. Reducing Effect of Thermal Noise

The adaptive noise cancellation technique has been originally

used to extract a narrowband signal from white random noise

[5]. This implies a double benefit of using the ANC at the output

of a sigmadelta modulator: ANC not only attenuates the high-

pass quantization noise but also removes the white random noisecaused by the thermal noise of analog devices. In a conven-

tional design of a sigmadelta modulator, the analog circuit is

designed so that thermal noise of the circuit is less than the

quantization noise of the converter. This results in large capac-

itors in switched-capacitor implementation as well as higher

power consumption and area. The fact that ANC reduces the

thermal noise can be exploited to relax the requirements on

analog circuits and to decrease the power consumption. The re-

duction of thermal noise is shown in Fig. 9 for the modulator de-

signed for the WLAN system. An input-referred thermal noise

of nV Hz is chosen. The SNR of the sigmadelta

modulator becomes 63 dB due to the additional thermal noise.

A 45th-order adaptive FIR filter is used in the ANC block to ex-tract the signal from the thermal and the quantization noise. A

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Fig. 9. Outputspectrum of the sigmadelta ADC designed for the WLAN stan-dard in the presence of thermal noise with and without using an ANC block.

comparison of the output spectrum for the ADC, equipped with

an ANC block and the one without ANC, is shown in Fig. 9 and

the results show that the SNR improves from 63 to 71.4 dB.

V. PRACTICAL CONSIDERATIONS

A. Nonlinearity of the Adaptive Filter

The derivation of the ANC transfer function presented in

Section III and in the Appendix was accomplished based on

the assumption that the adaptive filter coefficients have already

converged to theiroptimum Wiener solution. Under this assump-

tion, the ANC block with transfer function (7) is a discrete linearsystem and should not cause any distortion or intermodulation

product.

In fact, the LMS filter weights are the sum of the time-in-

variant Wiener coefficients and a time-varying misadjustment

component [11]

(16)

Therefore, the output filter expression in (9) should be revised

as

(17)

where is the Wiener solution as given in (9) and

is the misadjustment term. can be reduced by choosing

a smaller step size for the LMS algorithm that updates the filter

weights. A smaller step size, however, results in a slower con-

vergence rate. The effect of the misadjustment term in causing

nonlinearity is depicted in Fig. 10 which shows the result of

the two-tone test. As shown in this figure, when data are taken

before the complete convergence of the filter taps, large spurs

caused by intermodulation products and harmonic terms are

seen in the output spectrum. The output spectrum is also plotted

for the data taken after coefficients convergence,1 where the in-

termodulation products are not visible anymore.

1Filter coefficients converge after approximately 10 000 samples as depictedin Fig. 7.

Fig. 10. Two-tonetest on thesigmadelta ADC withan ANC usedfor the GSMsystem.

Fig. 11. CSD accumulative multiplication unit [12].

B. Implementation and Digital Complexity

The ANC block shown in Fig. 1 involves three major

calculations.

1) Calculating filter output

(18)

2) Calculating error signal

(19)

3) Updating filter coefficients

(20)

The three steps need multipliers, adders, and 1

subtractor. However, the multiplierless implementation is also

possible [12] and is preferable since it results in lower power

consumption. In multiplierless implementation, all numbers are

represented by the canonical signed digit (CSD) representations.

In CSD, numbers are sums or differences of powers of two

which enable replacing the multiplier with a shifter and adder.

Fig. 11 shows a CSD multiplication unit which is composed ofa shifter and an adder. Moreover, step size in (20) is chosen

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Fig. 12. ANC block using the multiplierless implementation of an adaptive FIR filter as proposed in [12].

as a power of two (i.e., ) to replace the multiplication

needed in step 3 with a shifter.

The multiplierless FIR adaptive filter can be implemented as

depicted in Fig. 12 using adders, 1 subtractor, shifters,

and CSD multiplier unit.

VI. CONCLUSION

In this paper, a simple adaptive noise cancellation tech-

nique has been presented that can be used to boost the SNR

of sigmadelta modulators. Mathematical derivation of the

system shows that ANC uses the statistical difference be-

tween the signal and noise and the fact that noise samples are

uncorrelated from each other whereas signal samples have

a much stronger correlation. Simulation results show that

ANC can effectively remove the inband as well as remaining

out-of-band quantization noise and reduces the thermal noise

of the sigmadelta modulator significantly. The proposed tech-

nique is all implemented digitally and, hence, does not bring

any complexity to the analog circuitry. Although this paper hasdemonstrated the application of ANC in a switched-capacitor

sigmadelta ADC, it is as well applicable to other oversampling

data converters (e.g., continuous-time sigmadelta modulators).

APPENDIX

It was shown in Section III that the ANC block, including

the adaptive FIR filter, can be approximated with a linear time-

invariant (LTI) system with a transfer function givenin (11). The

LTI assumption is true at steady state where the filter coefficients

have been already converged to their optimum values given by

the Wiener solution as

(A.1)

In this equation, is the auto-correlation matrix of the input

signal and is the cross-correlation vector between the filter

input and the desired signal of the filter.

The following steps show the derivations of these two

matrices.

Calculate Autocorrelation Matrix : is input to the

adaptive filter and is composed of the sinusoidal signal andthe quantization noise shaped by the sigmadelta modulator.

Since signal and noise are uncorrelated, the autocorrelation of

the input data would be sum of autocorrelation of the signal and

autocorrelation of the quantization noise

(A.2)

First, we calculate the autocorrelation of the signal. Considering

a sinusoidal signal

(A.3)

is a random phase uniformly distributed on .

The autocorrelation is calculated as

(A.4)

The autocorrelation matrix is a symmetric matrix

with each component calculated as in (A.4)

(A.5)

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where is a matrix defined as

(A.6)

In order to calculate autocorrelation of noise, it should be noted

that noise here is the shaped quantization noise. Assuming a

white uniformly distributed quantization noise, the power spec-

tral density of the quantization noise is given by

(A.7)

After being shaped by the noise transfer function of the

th-order sigmadelta modulator, the power spectral density

would be

(A.8)

Substituting in (A.7) results in

(A.9)

The autocorrelation function and power spectral density of a sta-

tionary stochastic process form a Fourier transform pair. There-

fore, knowing the power spectral density of noise, the autocor-

relation of noise is found as

(A.10)

However, different samples of noise are uncorrelated and, there-

fore, (A.10) is zero for all nonzero values of and the autocor-

relation matrix of noise will be

(A.11)

(A.12)

Finally, the autocorrelation matrix of filter input data can

be written using (A.2), (A.5), and (A.11)

(A.13)

Calculate Cross-Correlation Vector: The cross-correlation

vector is calculated as the correlation between the desired signal

and the input signal, which is the delayed version of the de-

sired signal by samples. Since noise components are uncor-

related from each other, only signal components will appear in

the cross-correlation vector

(A.14)

or in matrix form

(A.15)

where is defined as in (A.6) and is equal to

(A.16)

In order to find the filter coefficients using (A.1), needs

to be calculated. Using inverse lemma and (A.13), can be

written as

(A.17)

Therefore, filter coefficients are given by

(A.18)

where the term in parenthesis is

(A.19)

where and are

(A.20)

(A.21)

Finally, the transfer function of the ANC system defined in (11)

can be written as

(A.22)

with

(A.23)

Substituting using (A.18)

(A.24)

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Using (A.6), (A.11), (A.16), and (A.23) to substitute for , ,

, and , respectively, (A.24) results in

(A.25)

where

(A.26)

(A.27)

Calculate SNR Improvement: This section calculates SNR

improvement caused by the ANC block. Since the desired input

signal is sinusoidal with amplitude , the input signal power is

(A.28)

The signal power at the output of ANC block is given by

(A.29)

For large values of , hence

. The noise component of the input signal is the high-

pass quantization noise of the sigmadelta modulator with noise

power given by

(A.30)

The total noise power at the output of ANC is equal to

(A.31)

which can be also expressed as

(A.32)

where

(A.33)

Therefore, noise is reduced by a factor of

(A.34)

From (A.34), it is seen that SNR improvement depends on

the value of . This integral has a simple

closed-form expression for where

(A.35)

and

(A.36)

which allows the SNR improvement to be equal to

(A.37)

ACKNOWLEDGMENT

The authors would like to thank the reviewers for their valu-

able comments and suggestions that have been incorporated into

this paper.

REFERENCES

[1] A. Tabatabaei, K. Onodera, M. Zargari, H. Samavati, and D. Su, Adual channel

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with 40 MHz aggregate signal bandwidth, inProc. IEEE Int. Solid State Conf., 2003, pp. 6667.

[2] A. Rusu and H. Tenhunen, A multi-bit sigma delta modulator forwideband applications, in Proc. 9th Int. Conf. Electron. Circuits Syst.,Sep. 2002, vol. 1, pp. 335338.

[3] G. Keratiotis et al., A novel method for periodic interference suppres-sion on local telephone loops, IEEE Trans. Circuits Syst. I: Fundam.Theory Appl., vol. 47, no. 7, pp. 10961100, Jul. 2000.

[4] M. M. Sondhi, An adaptive echo canceller, Bell Syst. Tech. J., vol.46, pp. 497511, Mar. 1967.

[5] J. R. Treicher, The spectral line enhancerThe concept, an imple-mentation, and an application, Ph.D. dissertation, Dept. Elect. Eng.,Stanford Univ., Stanford, CA, 1977.

[6] P. Handel, Predictive digital filtering of sinusoidal signals, IEEETrans. Signal Process., vol. 46, no. 2, pp. 364 374, Feb. 1998.

[7] M. Ghogho, M. Ibnkahla, and N. J. Bershad, Analytic behavior of theLMS adaptive line enhancer for sinusoids corrupted by multiplicativeand additive noise, IEEE Trans. Signal Process., vol. 46, no. 9, pp.23862393, Sep. 1998.

[8] L. J. Griffiths, An adaptive noise canceling procedure for multidimen-sional systems, presented at the IEEE Circuits Syst. Conf., Asilomer,CA, Nov. 1976.

[9] B. J. Farahani, Adaptive digital calibration techniques for high speedhigh resolution sigma delta ADCs for broadband wireless applica-tions, Ph.D. dissertation, Dept. Elect. Comp. Eng., The Ohio StateUniv., Columbus, OH, 2005.

[10] A. Ghazel, L. Naviner, and K. Grati, On design and implementationof a decimation filter for multistandard wireless transceivers, IEEETrans. Wireless Commun., vol. 1, no. 4, pp. 558562, Oct. 2002.

[11] M. Reuter and J. R. Zeidler, Nonlinear effects in LMS adaptive equal-izers,IEEE Trans. Signal Process., vol. 47, no. 6, pp. 15701579, Jun.1999.

[12] L. S. DeBrunner, Y. Wang, V. DeBrunner, and M. Tull, Multiplierlessimplementation of adaptive FIR filters, in Proc. 37th Asilomar Conf.Signals, Syst. Comput., Nov. 2003, vol. 2, pp. 22322236.

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Bahar Jalali-Farahani (M02)receivedthe B.S. andM.S. degrees in electrical engineering from Univer-sity of Tehran, Tehran, Iran, in 1996 and 1999, re-spectively, and the Ph.D. degree in electrical engi-neering from The Ohio State University, Columbus,in 2005.

She was with the Advanced Technology, DataConverter Research Group, Freescale, Tempe, AZ,

in 2005, where she was working on the calibrationtechniques for high-performance analog-to-digitalconverters. She became an Assistant Professor with

Arizona State University, Tempe, in 2006. Her research interests are in signal-processing techniques for high-performance analog circuits, low-power low-

voltage analog design, and analog-to-digital and digital-to-analog converters.

Mohammed Ismail (F97) received the B.S. andM.S. degrees in electronics and communicationsfrom Cairo University, Giza, Egypt, and the Ph.D.degree in electrical engineering from the Universityof Manitoba, Winnipeg, MB, Canada.

Hehasmorethan25 years ofexperiencein R&Dinthe fields of analog, RF, and mixed-signal integratedcircuits (ICs). He has held several positions in both

industry and academia and has served as a corporateconsultant to nearly 30 companies in the U.S., Eu-rope, and the Far East. He is Professor of Electrical

and Computer Engineering and theFounding Director of the Analog Very LargeScaleIntegration Lab, The Ohio StateUniversity, Columbus. His currentinterestlies in research involving digitally programmable/configurable fully integratedradios with a focus on low-voltage/low-power firstpass solutions for third-gen-eration (3G) and fourth-generation (4G) wireless handhelds. He publishes in-tensively in this area and has been awarded 11 patents. He has coedited andcoauthored several books including a text on Analog VLSI Signal and Informa-tion Processing (McGraw-Hill, 1994). He authored Radio Design in NanometerTechnologies (Springer, 2007). He advised the thesis work of 43 Ph.D. studentsand of more than 85 M.Sc. students. He cofounded ANACAD-Egypt (now partof Mentor Graphics Inc.) and Firstpass Technologies Inc., a developer of CMOSradio and mixed-signal Internet protocols for handheld wireless applications. Heis the Founder of the International Journal of Analog Integrated Circuits andSignal Processing (Springer) and serves as the Editor-In-Chief.

Dr.Ismail hasbeen therecipient of several awardsincluding theU.S. NationalScience Foundation Presidential Young Investigator Award, the U.S. Semicon-ductor Research Corp Inventor Recognition Awards in 1992 and 1993, The Col-lege of Engineering Lumley Research Award in 1992, 1997, 2002, and 2007anda Fulbright/Nokia fellowship Award in 1995.He has served as Associate Editorfor many IEEE TRANSACTIONS, is on the International Advisory Boards of sev-eral journals, and was on the Board of Governors of the IEEE Circuits and Sys-tems Society.

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