T.Y. Al-Naffouri

King Fahd University of Petroleum and Minerals, Az̧ Z̧ahrān, Eastern Province, Saudi Arabia

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Publications (80)80.32 Total impact

  • Source
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    ABSTRACT: In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifier's nonlinear distortions.
    Signal Processing. 01/2014; 97:282–293.
  • Tareq Y. Al-Naffouri, Ahmed A. Quadeer, Giuseppe Caire
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    ABSTRACT: Orthogonal Frequency Division Multiplexing (OFDM) is a modulation scheme that is widely used in wired and wireless communication systems. While OFDM is ideally suited to deal with frequency selective channels and AWGN, its performance may be dramatically impacted by the presence of impulse noise. In fact, very strong noise impulses in the time domain might result in the erasure of whole OFDM blocks of symbols at the receiver. Impulse noise can be mitigated by considering it as a sparse signal in time, and using recently developed algorithms for sparse signal reconstruction. We propose an algorithm that utilizes the guard band null subcarriers for the impulse noise estimation and cancellation. Instead of relying on ell_1 minimization as done in some popular general-purpose compressive sensing schemes, the proposed method jointly exploits the specific structure of this problem and the available a priori information for sparse signal recovery. The computational complexity of the proposed algorithm is very competitive with respect to sparse signal reconstruction schemes based on ell_1 minimization. The proposed method is compared with respect to other state-of-the-art methods in terms of achievable rates for an OFDM system with impulse noise and AWGN.
    IEEE Transactions on Communications 01/2014; 62(3):976-989. · 1.75 Impact Factor
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    ABSTRACT: Localization systems are most often based on time delay estimation (TDE) techniques. TDE techniques based on channel impulse response (CIR) are effective in reverberant environment such as indoors. A recently developed algorithm called Orthogonal Clustering (OC) algorithm is one such algorithm that estimates the CIR utilizing a sparse signal reconstruction approach. OC is based on low complexity Bayesian method utilizing the sparsity constraint, the sensing matrix structure and the a priori statistical information. In practical systems several parameters affect the performance of a localization system based on OC TDE. Therefore, it is necessary to analyze the performance of an algorithm when certain parameters vary. In this paper we investigate the effect of variations in different parameters on the performance of the OC algorithm used in an impulsive acoustic source localization (IASL) system.
    The First International Conference on Communications, Signal Processing, and their Applications; 02/2013
  • Furrukh Sana, Tarig Ballal, Tareq Y. Al-Naffouri, Ibrahim Hoteit
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    ABSTRACT: In this paper, we present a comprehensive scheme for wireless monitoring of the respiratory movements in humans. Our scheme overcomes the challenges low signal-to-noise ratio, background clutter and high sampling rates. It is based on the estimation of the ultra-wideband channel impulse response. We suggest techniques for dealing with background clutter in situations when it might be time variant. We also present a novel methodology for reducing the required sampling rate of the system significantly while achieving the accuracy offered by the Nyquist rate. Performance results from simulations conducted with pre-recorded respiratory signals demonstrate the robustness of our scheme for tackling the above challenges and providing a low-complexity solution for the monitoring of respiratory movements.
    Biomedical Signal Processing and Control 01/2013; · 1.07 Impact Factor
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    A. Ali, O. Hammi, T.Y. Al-Naffouri
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    ABSTRACT: Linearization of user equipment power amplifiers driven by orthogonal frequency division multiplexing signals is addressed in this paper. Particular attention is paid to the power efficient operation of an orthogonal frequency division multiple access cognitive radio system and realization of such a system using compressed sensing. Specifically, precompensated overdriven amplifiers are employed at the mobile terminal. Over-driven amplifiers result in in-band distortions and out of band interference. Out of band interference mostly occupies the spectrum of inactive users, whereas the in-band distortions are mitigated using compressed sensing at the receiver. It is also shown that the performance of the proposed scheme can be further enhanced using multiple measurements of the distortion signal in single-input multi-output systems. Numerical results verify the ability of the proposed setup to improve error vector magnitude, bit error rate, outage capacity and mean squared error.
    Emerging and Selected Topics in Circuits and Systems, IEEE Journal on. 01/2013; 3(4):508-520.
  • A.A. Quadeer, M.S. Sohail, T.Y. Al-Naffouri
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    ABSTRACT: This paper presents a compressed sensing based method to suppress impulse noise in digital subscriber line (DSL). The proposed algorithm exploits the sparse nature of the impulse noise and utilizes the null carriers, already available in all practical DSL systems, for its estimation and cancelation. Specifically, compressed sensing is used for a coarse estimate of the impulse position, an a priori information based maximum aposteriori probability (MAP) metric for its refinement, followed by least squares (LS) or minimum mean square error (MMSE) estimation for estimating the impulse amplitudes. Simulation results show that the proposed scheme achieves higher rate as compared to other known sparse estimation algorithms in literature. The paper also demonstrates the superior performance of the proposed scheme compared to the ITU-T G992.3 standard that utilizes RS-coding for impulse noise refinement in DSL signals.
    Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on; 01/2013
  • M. Omer, A.A. Quadeer, T.Y. Al-Naffouri, M.S. Sharawi
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    ABSTRACT: This paper presents an L-shaped microphone array configuration for a robust 2-D localization of an impulsive acoustic source in an indoor environment. The localization technique relies on a recently proposed time delay estimation technique based on the orthogonal clustering algorithm (TDE-OC) which is designed to work under room reverberant conditions and at low sampling rates. The TDE-OC method finds the TDEs from the sparse room impulse response (RIR) signal. The TDE's obtained from RIR adds to the robustness of the TDE-OC method against room reverberations while the low sampling rates requirement reduces the hardware and computational complexity and relaxes the communication link between the microphones and the centralized location. Experimental results show the robustness of this method in a reverberant environment with low sampling rates, when compared with the generalized cross correlation method.
    Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on; 01/2013
  • Source
    Ahmed A. Quadeer, Tareq Y. Al-Naffouri
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    ABSTRACT: Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is relatively low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at a low sparsity rate.
    IEEE Transactions on Signal Processing 07/2012; 60(12). · 2.81 Impact Factor
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    ABSTRACT: This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm.
    IEEE Transactions on Signal Processing 07/2012; 60(12). · 2.81 Impact Factor
  • M.S. Sohail, Tareq Y. Al-Naffouri, S.N. Al-Ghadhban
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    ABSTRACT: This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing (ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous with the frequency grid of the ZP-OFDM system. The proposed structure based technique uses the fact that the NBI signal is sparse as compared to the ZP-OFDM signal in the frequency domain. The structure is also useful in reducing the computational complexity of the proposed method. The paper also presents a data aided approach for improved NBI estimation. The suitability of the proposed method is demonstrated through simulations.
    Signal Processing Advances in Wireless Communications (SPAWC), 2012 IEEE 13th International Workshop on; 01/2012
  • Source
    H Ali, M S Sharawi, T Y Al-Naffouri
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    ABSTRACT: Localization systems are most often based on time delay estimation (TDE) techniques. TDE techniques based on channel impulse response (CIR) are effective in reverberant environment such as indoors. A recently developed algorithm called Orthogonal Clustering (OC) algorithm is one such algorithm that estimates the CIR utilizing a sparse signal reconstruction approach. OC is based on low complexity Bayesian method utilizing the sparsity constraint, the sensing matrix structure and the a priori statistical information. In practical systems several parameters affect the performance of a localization system based on OC TDE. Therefore, it is necessary to analyze the performance of an algorithm when certain parameters vary. In this paper we investigate the effect of variations in different parameters on the performance of the OC algorithm used in an impulsive acoustic source localization (IASL) system.
    Telecommunications (ICT), 2012 19th International Conference on; 01/2012
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    Ebrahim B. Al-Safadi, Tareq Y. Al-Naffouri
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    ABSTRACT: This work establishes the design, analysis, and fine-tuning of a peak-to-average-power-ratio (PAPR) reducing system, based on compressed sensing (CS) at the receiver of a peak-reducing sparse clipper applied to an orthogonal frequency-division multiplexing (OFDM) signal at the transmitter. By exploiting the sparsity of clipping events in the time domain relative to a predefined clipping threshold, the method depends on partially observing the frequency content of the clipping distortion over reserved tones to estimate the remaining distortion.
    IEEE Transactions on Signal Processing 01/2012; 60(7):3834-3839. · 2.81 Impact Factor
  • M. Omer, A.A. Quadeer, M.S. Sharawi, Tareq Y. Al-Naffouri
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    ABSTRACT: This paper presents a new method of time delay estimation (TDE) using low sample rates of an impulsive acoustic source in a room environment. The proposed method finds the time delay from the room impulse response (RIR) which makes it robust against room reverberations. The RIR is considered a sparse phenomenon and a recently proposed sparse signal reconstruction technique called orthogonal clustering (OC) is utilized for its estimation from the low rate sampled received signal. The arrival time of the direct path signal at a pair of microphones is identified from the estimated RIR and their difference yields the desired time delay. Low sampling rates reduce the hardware and computational complexity and decrease the communication between the microphones and the centralized location. The performance of the proposed technique is demonstrated by numerical simulations and experimental results.
    Information Science, Signal Processing and their Applications (ISSPA), 2012 11th International Conference on; 01/2012
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    S.F. Ahmed, T.Y. Al-Naffouri, A.H. Muqaibel
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    ABSTRACT: The paper addresses the problem of channel estimation in Impulse-Radio Ultra-Wideband (IR-UWB) communication system. The IEEE 802.15.4a channel model is used where the channel is assumed to be Linear Time Invariant (LTI) and thus the problem of channel estimation becomes the estimation of the sparse channel taps and their delays. Since, the bandwidth of the signal is very large, Nyquist rate sampling is impractical, therefore, we propose to estimate the channel taps from the sub-sampled versions of the received signal profile. We adopt the Bayesian framework to estimate the channel support by incorporating the a priori multipath arrival time statistics. In the first approach, we adopt a two-step method by employing Compressive Sensing to obtain coarse estimates and then refine them by applying Maximum A Posteriori (MAP) criterion. In the second approach, we develop a Low-Complexity MAP (LC-MAP) estimator. The computational complexity is reduced by identifying nearly orthogonal clusters in the received profile and by leveraging the structure of the sensing matrix.
    Ultra-Wideband (ICUWB), 2011 IEEE International Conference on; 10/2011
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    Ahmed Abdul Quadeer, Syed Faraz Ahmed, Tareq Y. Al-Naffouri
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    ABSTRACT: In this paper, we present a fast Bayesian method for sparse signal recovery that makes a collective use of the sparsity information, a priori statistical properties, and the structure involved in the problem to obtain near optimal estimates at very low complexity. Specifically, we utilize the rich structure present in the sensing matrix encountered in many signal processing applications to develop a fast reconstruction algorithm when the statistics of the sparse signal are non- Gaussian or unknown. The proposed method outperforms the widely used convex relaxation approaches as well as greedy matching pursuit techniques all while operating at a much lower complexity.
    Communication, Control, and Computing (Allerton), 2011 49th Annual Allerton Conference on; 09/2011
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    T.Y. Al-Naffouri, A.A. Quadeer, F.F. Al-Shaalan, H. Hmida
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    ABSTRACT: Impulsive noise is the bottleneck that determines the maximum length of the DSL. Impulsive noise seldom occurs in DSL but when it occurs, it is very destructive and results in dropping the affected DSL symbols at the receiver as they cannot be recovered. By considering impulsive noise a sparse vector, recently developed sparse reconstruction algorithms can be utilized to combat it. We propose an algorithm that utilizes the null carriers for the impulsive noise estimation and cancellation. Specifically, we use compressive sampling for a coarse estimate of the impulse position, an a priori information based MAP metric for its refinement, followed by MMSE estimation for estimating the impulse amplitudes. We also present a comparison of the achievable rate in DSL using our algorithm and recently developed algorithms for sparse signal reconstruction.
    Circuits and Systems (ISCAS), 2011 IEEE International Symposium on; 06/2011
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    Tareq Y. Al-Naffouri, Muhammad Moinuddin, Muhammad S. Sohail
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    ABSTRACT: This paper presents a novel approach for evaluating the mean behavior of the well known normalized least mean squares (NLMS) adaptive algorithm for a circularly correlated Gaussian input. The mean analysis of the NLMS algorithm requires the calculation of some normalized moments of the input. This is done by first expressing these moments in terms of ratios of quadratic forms of spherically symmetric random variables and finding the cumulative density function (CDF) of these variables. The CDF is then used to calculate the required moments. As a result, we obtain explicit expressions for the mean behavior of the NLMS algorithm.
    IEEE Signal Processing Letters 01/2011; 18:7-10. · 1.67 Impact Factor
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    K. M. Zahidul Islam, Tareq Y. Al-Naffouri, Naofal Al-Dhahir
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    ABSTRACT: We consider comb-type OFDM transmission over doubly-selective channels. Given a fixed number and total power of the pilot subcarriers, we show that the MMSE-optimum pilot design consists of identical equally-spaced clusters where each cluster is zero-correlation-zone sequence.
    IEEE Transactions on Communications 01/2011; 59:930-935. · 1.75 Impact Factor
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    K. M. Zahidul Islam, Tareq Y. Al-Naffouri, Naofal Al-Dhahir
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    ABSTRACT: Under high mobility, the orthogonality between sub-carriers in an OFDM symbol is destroyed resulting in severe intercarrier interference (ICI). We present a novel algorithm to estimate the channel and ICI coefficients by exploiting the channel’s time and frequency correlations and the (approximately) banded structure of the frequency-domain channel matrix. In addition, we invoke the asymptotic equivalence of Toeplitz and circulant matrices to reduce the dimensionality of the channel estimation problem by retaining the dominant terms only in an offline eigen-decomposition. Furthermore, we show that the asymptotically MMSE-optimum pilot design consists of identical equally-spaced frequency-domain clusters whose size is determined by the channel Doppler spread. Comparisons of our proposed algorithm with a widely-cited recent algorithm demonstrate a significant performance advantage at a comparable real-time complexity.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011, May 22-27, 2011, Prague Congress Center, Prague, Czech Republic; 01/2011 · 4.63 Impact Factor
  • Source
    Tareq Y. Al-Naffouri, Ahmed A. Quadeer, Giuseppe Caire
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    ABSTRACT: Impulsive noise is the bottleneck that limits the distance at which DSL communications can take place. By considering impulsive noise a sparse vector, recently developed sparse reconstruction algorithms can be utilized to combat it. We propose an algorithm that utilizes the guard band null carriers for the impulsive noise estimation and cancellation. Instead of relying on ℓ1 minimization as done in some popular general-purpose compressive sensing (CS) schemes, the proposed method exploits the structure present in the problem and the available a priori information jointly for sparse signal recovery. The computational complexity of the proposed algorithm is very low as compared to the sparse reconstruction algorithms based on ℓ1 minimization. A performance comparison of the proposed method with other techniques, including ℓ1 minimization and another recently developed scheme for sparse signal recovery, is provided in terms of achievable rates for a DSL line with impulse noise estimation and cancellation.
    01/2011;

Publication Stats

521 Citations
80.32 Total Impact Points

Institutions

  • 2005–2014
    • King Fahd University of Petroleum and Minerals
      • Department of Electrical Engineering
      Az̧ Z̧ahrān, Eastern Province, Saudi Arabia
  • 2007
    • California Institute of Technology
      • Department of Electrical Engineering
      Pasadena, CA, United States
  • 1998–2004
    • Stanford University
      • Department of Electrical Engineering
      Stanford, CA, United States
    • Georgia Institute of Technology
      • School of Electrical & Computer Engineering
      Atlanta, GA, United States
  • 2001
    • University of California, Los Angeles
      • Department of Electrical Engineering
      Los Angeles, CA, United States