T.Y. Al-Naffouri

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

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Publications (100)93.88 Total impact

  • Proceedings of IEEE GLOBECOM 2014; 12/2014
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    Anum Ali, Ali Al-Zahrani, Tareq Y Al-Naffouri, Ayman Naguib
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    ABSTRACT: High peak-to-average power ratio is one of the major draw-backs of orthogonal frequency division multiplexing (OFDM). Clipping is the simplest peak reduction scheme, however, it requires clipping mitigation at the receiver. Recently com-pressed sensing has been used for clipping mitigation (by exploiting the sparse nature of clipping signal). However, clipping estimation in multi-user scenario (i.e., OFDMA) is not straightforward as clipping distortions overlap in fre-quency domain and one cannot distinguish between distor-tions from different users. In this work, a collaborative clip-ping removal strategy is proposed based on joint estimation of the clipping distortions from all users. Further, an effective data aided channel estimation strategy for clipped OFDM is also outlined. Simulation results are presented to justify the effectiveness of the proposed schemes.
    International Conference on Acoustics, Speech and Signal Processing (ICASSP); 05/2014
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    ABSTRACT: In this paper, we introduce a game theoretic framework for studying the problem of minimizing the delay of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets without a central controller. Game theory is employed herein as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. We model the session by self-interested players in a non-cooperative potential game. The utility functions are designed such that increasing individual payoff results in a collective behavior achieving both a desirable system performance in a shared network environment and the Nash bargaining solution. Three games are developed: the first aims to reduce the completion time, the second to reduce the maximum decoding delay and the third the sum decoding delay. We improve these formulations to include punishment policy upon collision occurrence and achieve the Nash bargaining solution. Through extensive simulations, our framework is tested against the best performance that could be found in the conventional point-to-multipoint (PMP) recovery process in numerous cases: first we simulate the problem with complete information. We, then, simulate with incomplete information and finally we test it in lossy feedback scenario. Numerical results show that our formulation with complete information largely outperforms the conventional PMP scheme in most situations and achieves a lower delay. They also show that the completion time formulation with incomplete information also outperforms the conventional PMP.
    04/2014;
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    ABSTRACT: In this paper, we introduce a game theoretic framework for studying the problem of minimizing the completion time of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets without a central controller. Game theory is employed herein as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. We model the session by self-interested players in a non-cooperative potential game. The utility function is designed such that increasing individual payoff results in a collective behavior achieving both a desirable system performance in a shared network environment and the Pareto optimal solution. Through extensive simulations, our approach is compared to the best performance that could be found in the conventional point-to-multipoint (PMP) recovery process. Numerical results show that our formulation largely outperforms the conventional PMP scheme in most practical situations and achieves a lower delay.
    04/2014;
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    ABSTRACT: In this paper, we consider the problem of minimizing the maximum broadcast decoding delay experienced by all the receivers of generalized instantly decodable network coding (IDNC). Unlike the sum decoding delay, the maximum decoding delay as a definition of delay for IDNC allows a more equitable distribution of the delays between the different receivers and thus a better Quality of Service (QoS). In order to solve this problem, we first derive the expressions for the probability distributions of maximum decoding delay increments. Given these expressions, we formulate the problem as a maximum weight clique problem in the IDNC graph. Although this problem is known to be NP-hard, we design a greedy algorithm to perform effective packet selection. Through extensive simulations, we compare the sum decoding delay and the max decoding delay experienced when applying the policies to minimize the sum decoding delay [1] and our policy to reduce the max decoding delay. Simulations results show that our policy gives a good agreement among all the delay aspects in all situations and outperforms the sum decoding delay policy to effectively minimize the sum decoding delay when the channel conditions become harsher. They also show that our definition of delay significantly improve the number of served receivers when they are subject to strict delay constraints.
    04/2014;
  • EURASIP journal on advances in signal processing 01/2014; · 0.89 Impact Factor
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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: In this paper, we study the broadcast decoding delay performance of generalized instantly decodable network coding (G-IDNC) in the lossy feedback scenario. The problem is formulated as a maximum weight clique problem over the G-IDNC graph in [1]. In order to further minimize the decoding delay, we introduce in this paper the lossy G-IDNC graph (LG-IDNC). Whereas the G-IDNC graph represents only doubtless combinable packets, the LG-IDNC graph represents also uncertain packet combinations when the expected decoding delay of the encoded packet is lower than the individual expected decoding delay of each packet encoded in it. Since the maximum weight clique problem is known to be NP-hard, we use the heuristic introduced in [2] to discover the maximum weight clique in the LG-IDNC graph and finally we compare the decoding delay performance of LG-IDNC and G-IDNC graphs through extensive simulations. Numerical results show that our new LG-IDNC graph formulation outperforms the G-IDNC graph formulation in all situations and achieves significant improvement in the decoding delay especially when the feedback erasure probability is higher than the packet erasure probability.
    Wireless Communications Letters, IEEE. 11/2013; 3(3).
  • Mudassir Masood, Tareq Y. Al-Naffouri
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    ABSTRACT: A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.
    IEEE Transactions on Signal Processing 11/2013; 61(21):5298-5309. · 2.81 Impact Factor
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    ABSTRACT: One of the main drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Most of the PAPR reduction techniques require transmitter-based processing. However, we propose a receiver-based low-complexity clip-ping signal recovery method. This method is able to i) reduce PAPR via a simple clipping scheme, ii) use a Bayesian recov-ery algorithm to reconstruct the distortion signal with high accuracy, and iii) is energy efficient due to low complexity. The proposed method is robust against variation in noise and signal statistics. The method is enhanced by making use of all prior information such as, the locations and the phase of the non-zero elements of the clipping signal. Simulation results demonstrate the superiority of using the proposed algorithm over other recovery algorithms.
    European Signal Processing Conference (EUSIPCO); 09/2013
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    ABSTRACT: In this paper, we consider the problem of minimizing the multicast decoding delay of generalized instantly decodable network coding (G-IDNC) over persistent forward and feedback erasure channels with feedback intermittence. In such an environment, the sender does not always receive acknowledgement from the receivers after each transmission. Moreover, both the forward and feedback channels are subject to persistent erasures, which can be modelled by a two state (good and bad states) Markov chain known as Gilbert-Elliott channel (GEC). Due to such feedback imperfections, the sender is unable to determine subsequent instantly decodable packets combination for all receivers. Given this harsh channel and feedback model, we first derive expressions for the probability distributions of decoding delay increments and then employ these expressions in formulating the minimum decoding problem in such environment as a maximum weight clique problem in the G-IDNC graph. We also show that the problem formulations in simpler channel and feedback models are special cases of our generalized formulation. Since this problem is NP-hard, we design a greedy algorithm to solve it and compare it to blind approaches proposed in literature. Through extensive simulations, our adaptive algorithm is shown to outperform the blind approaches in all situations and to achieve significant improvement in the decoding delay, especially when the channel is highly persistent
    08/2013;
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    ABSTRACT: In this paper, we study the multicast completion and decoding delay minimization problems of instantly decodable network coding (IDNC) in the case of lossy feedback. In such environments, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient partially blind policies that handle the completion and decoding delays of IDNC in such environment, we first extend the perfect feedback formulation in [2], [3] to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback, and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in [3]. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to derive the optimal policy to reduce the decoding delay in such lossy feedback environment. Results show that our proposed solution both outperforms other approaches and achieves a tolerable degradation even at relatively high feedback loss rates.
    IEEE Transactions on Wireless Communications 07/2013; · 2.42 Impact Factor
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    ABSTRACT: We address the distributed estimation of an unknown scalar parameter in Wireless Sensor Networks (WSNs). Sensor nodes transmit their noisy observations over multiple access channel to a Fusion Center (FC) that reconstructs the source parameter. The received signal is corrupted by noise and channel fading, so that the FC objective is to minimize the Mean-Square Error (MSE) of the estimate. In this paper, we assume sensor node observations to be correlated with the source signal and correlated with each other as well. The correlation coefficient between two observations is exponentially decaying with the distance separation. The effect of the distance-based correlation on the estimation quality is demonstrated and compared with the case of unity correlated observations. Moreover, a closed-form expression for the outage probability is derived and its dependency on the correlation coefficients is investigated. Numerical simulations are provided to verify our analytic results.
    04/2013;
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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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    ABSTRACT: Estimating unknown signal in Wireless Sensor Networks (WSNs) requires sensor nodes to transmit their observations of the signal over a multiple access channel to a Fusion Center (FC). The FC uses the received observations, which is corrupted by observation noise and both channel fading and noise, to find the minimum Mean Square Error (MSE) estimate of the signal. In this paper, we investigate the effect of the source-node correlation (the correlation between sensor node observations and the source signal) and the inter-node correlation (the correlation between sensor node observations) on the performance of the Linear Minimum Mean Square Error (LMMSE) estimator for three correlation models in the presence of channel fading. First, we investigate the asymptotic behavior of the achieved distortion (i.e., MSE) resulting from both the observation and channel noise in a non-fading channel. Then, the effect of channel fading is considered and the corresponding distortion outage probability, the probability that the distortion exceeds a certain value, is found. By representing the distortion as a ratio of indefinite quadratic forms, a closed-form expression is derived for the outage probability that shows its dependency on the correlation. Finally, the new representation of the outage probability allows us to propose an iterative solution for the power allocation problem to minimize the outage probability under total and individual power constraints. Numerical simulations are provided to verify our analytic results.
    IEEE Transactions on Signal Processing 01/2013; 61(24):6264-6275. · 2.81 Impact Factor
  • A. Douik, S. Sorour, M.-S. Alouini, T.Y. Al-Naffouri
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    ABSTRACT: In this paper, we study the effect of lossy intermittent feedback loss events on the multicast decoding delay performance of generalized instantly decodable network coding. These feedback loss events create uncertainty at the sender about the reception statues of different receivers and thus uncertainty to accurately determine subsequent instantly decodable coded packets. To solve this problem, we first identify the different possibilities of uncertain packets at the sender and their probabilities. We then derive the expression of the mean decoding delay. We formulate the Generalized Instantly Decodable Network Coding (G-IDNC) minimum decoding delay problem as a maximum weight clique problem. Since finding the optimal solution is NP-hard, we design a variant of the algorithm employed in [1]. Our algorithm is compared to the two blind graph update proposed in [2] through extensive simulations. Results show that our algorithm outperforms the blind approaches in all the situations and achieves a tolerable degradation, against the perfect feedback, for large feedback loss period.
    Wireless and Mobile Computing, Networking and Communications (WiMob), 2013 IEEE 9th International Conference on; 01/2013
  • M. Masood, T.Y. Al-Naffouri
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    ABSTRACT: A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 01/2013 · 4.63 Impact Factor
  • S. Sorour, T.Y. Al-Naffouri, M.-S. Alouini
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    ABSTRACT: In this paper, we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in underlay cellular cognitive radio networks. This scheme allows the collocated primary and cognitive radio base-stations to collaborate with each other, in order to minimize their own and each other's packet recovery overheads, and thus improve their throughput, without any coordination between them. This non-coordinated collaboration is done using a novel multi-layer instantly decodable network coding scheme, which guarantees that each network's help to the other network does not result in any degradation in its own performance. It also does not cause any violation to the primary networks interference thresholds in the same and adjacent cells. Yet, our proposed scheme both guarantees the reduction of the recovery overhead in collocated primary and cognitive radio networks, and allows early recovery of their packets compared to non-collaborative schemes. Simulation results show that a recovery overhead reduction of 15% and 40% can be achieved by our proposed scheme in the primary and cognitive radio networks, respectively, compared to the corresponding non-collaborative scheme.
    Communications (ICC), 2013 IEEE International Conference on; 01/2013

Publication Stats

525 Citations
93.88 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