Tareq Y. Al-Naffouri

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

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Publications (157)140.94 Total impact

  • Ahmed Douik · Sameh Sorour · Tareq Y. Al-Naffouri · M.-S. Alouini
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    ABSTRACT: This paper studies the delay reduction problem for instantly decodable network coding (IDNC)-based device-to-device (D2D) communication-enabled networks. Unlike conventional point-to-multipoint (PMP) systems in which the wireless base station has the sufficient computation abilities, D2D networks rely on battery-powered operations of the devices. Therefore, a particular emphasis on the computation complexity needs to be addressed in the design of delay reduction algorithms for D2D networks. While most of the existing literature on IDNC directly extend the delay reduction PMP schemes, known to be NP-hard, to the D2D setting, this paper proposes to investigate and minimize the complexity of such algorithms for battery-powered devices. With delay minimization problems in IDNC-based systems being equivalent to a maximum weight clique problems in the IDNC graph, the presented algorithms, in this paper, can be applied to different delay aspects. This paper introduces and focuses on the reduction of the maximum value of the decoding delay as it represents the most general solution. The complexity of the solution is reduced by first proposing efficient methods for the construction, the update, and the dimension reduction of the IDNC graph. The paper, further, shows that, under particular scenarios, the problem boils down to a maximum clique problem. Due to the complexity of discovering such maximum clique, the paper presents a fast selection algorithm. Simulation results illustrate the performance of the proposed schemes and suggest that the proposed fast selection algorithm provides appreciable complexity gain as compared to the optimal selection one, with a negligible degradation in performance. In addition, they indicate that the running time of the proposed solution is close to the random selection algorithm.
    No preview · Article · Dec 2016 · Journal on Advances in Signal Processing

  • No preview · Article · Jan 2016 · IEEE Transactions on Communications
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    ABSTRACT: Recent studies on cloud-radio access networks (CRANs) assume the availability of a single processor (cloud) capable of managing the entire network performance; inter-cloud interference is treated as background noise. This paper considers the more practical scenario of the downlink of a CRAN formed by multiple clouds, where each cloud is connected to a cluster of multiple-antenna base stations (BSs) via high-capacity wireline backhaul links. The network is composed of several disjoint BSs’ clusters, each serving a pre-known set of single-antenna users. To account for both inter-cloud and intra-cloud interference, the paper considers the problem of minimizing the total network power consumption subject to quality of service constraints, by jointly determining the set of active BSs connected to each cloud and the beamforming vectors of every user across the network. The paper solves the problem using Lagrangian duality theory through a dual decomposition approach, which decouples the problem into multiple and independent subproblems, the solution of which depends on the dual optimization problem. The solution then proceeds in updating the dual variables and the active set of BSs at each cloud iteratively. The proposed approach leads to a distributed implementation across the multiple clouds through a reasonable exchange of information between adjacent clouds. The paper further proposes a centralized solution to the problem. Simulation results suggest that the proposed algorithms significantly outperform the conventional per-cloud update solution, especially at high signal-to-interference-plus-noise ratio (SINR) target.
    Full-text · Conference Paper · Dec 2015
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    O. Hammi · A. Abdelhafiz · T. Y. Al-Naffouri · F. M. Ghannouchi

    Full-text · Conference Paper · Dec 2015
  • Ahmed Douik · Sameh Sorour · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
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    ABSTRACT: This paper considers the multicast decoding delay reduction problem for generalized instantly decodable network coding (G-IDNC) over persistent erasure channels with feedback imperfections. The feedback scenario discussed is the most general situation in which the sender does not always receive acknowledgments from the receivers after each transmission and the feedback communications are subject to loss. The decoding delay increment expressions are derived and employed to express the decoding delay reduction problem as a maximum weight clique problem in the G-IDNC graph. This paper provides a theoretical analysis of the expected decoding delay increase at each time instant. Problem formulations in simpler channel and feedback models are shown to be special cases of the proposed generalized formulation. Since finding the optimal solution to the problem is known to be NP-hard, a suboptimal greedy algorithm is designed and compared with blind approaches proposed in the literature. Through extensive simulations, the proposed algorithm is shown to outperform the blind methods in all situations and to achieve significant improvement, particularly for high time-correlated channels.
    No preview · Article · Nov 2015 · IEEE Transactions on Wireless Communications
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    Tarig Ballal · T. Y. Al-Naffouri
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    DESCRIPTION: IEEE Transactions on Communications, (accepted for publication) Sep 2015
    Full-text · Research · Oct 2015
  • Hayssam Dahrouj · Ahmed Douik · Frank Rayal · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
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    ABSTRACT: The rapid pace of demand for mobile data services and the limited supply of capacity in the current wireless access networks infrastructure are leading network operators to increase the density of base station deployments to improve network performance. This densification, made possible by small-cell deployment, also brings a novel set of challenges, specifically related to the cost of ownership, in which backhaul is of primary concern. This article proposes a cost-effective hybrid RF/free-space optical (FSO) solution to combine the advantages of RF backhauls (low cost, NLOS applications) and FSO backhauls (high-rate, low latency). To first illustrate the cost advantages of the RF backhaul solution, the first part of this article presents a business case of NLOS wireless RF backhaul, which has a low cost of ownership as compared to other backhaul candidates. RF backhaul, however, is limited by latency problems. On the other side, an FSO solution, which offers better latency and higher data rate than RF backhauls, remains sensitive to weather and nature conditions (e.g., rain, fog). To combine RF and FSO advantages, the second part of this article proposes a lowcost hybrid RF/FSO solution, wherein base stations are connected to each other using either optical fiber or hybrid RF/FSO links. This part addresses the problem of minimizing the cost of backhaul planning under reliability, connectivity, and data rate constraints, and proposes choosing the appropriate cost-effective backhaul connection between BSs (i.e., either OF or hybrid RF/FSO) using graph theory techniques.
    No preview · Article · Oct 2015 · IEEE Wireless Communications
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    M. Tamim Alkhodary · Tarig Ballal · T. Y. Al-Naffouri · Ali Muqaibel

    Full-text · Conference Paper · Aug 2015
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    Ahmed Douik · Salah A. Aly · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
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    ABSTRACT: This paper introduces a novel algorithm for cardinality, i.e., the number of nodes, estimation in large scale anonymous graphs using statistical inference methods. Applications of this work include estimating the number of sensor devices, online social users, active protein cells, etc. In anonymous graphs, each node possesses little or non-existing information on the network topology. In particular, this paper assumes that each node only knows its unique identifier. The aim is to estimate the cardinality of the graph and the neighbours of each node by querying a small portion of them. While the former allows the design of more efficient coding schemes for the network, the second provides a reliable way for routing packets. As a reference for comparison, this work considers the Best Linear Unbiased Estimators (BLUE). For dense graphs and specific running times, the proposed algorithm produces a cardinality estimate proportional to the BLUE. Furthermore, for an arbitrary number of iterations, the estimate converges to the BLUE as the number of queried nodes tends to the total number of nodes in the network. Simulation results confirm the theoretical results by revealing that, for a moderate running time, asking a small group of nodes is sufficient to perform an estimation of 95% of the whole network.
    Full-text · Article · Aug 2015
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    Full-text · Dataset · Aug 2015
  • M. Masood · L.H. Afify · T.Y. Al-Naffouri
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    ABSTRACT: We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.
    No preview · Article · Aug 2015
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    Alam Zaib · Mudassir Masood · Anum Ali · Weiyu Xu · Tareq Y. Al-Naffouri
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    DESCRIPTION: In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed ...
    Full-text · Research · Aug 2015
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    Ahmed Douik · Hayssam Dahrouj · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
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    ABSTRACT: The rapid pace of demand for mobile data services and the limited supply of capacity by the current wireless access networks infrastructure are leading network operators to increase the density of base station deployments, particularly in urban areas, to improve the network performance. This densification, made possible by small-cell deployment, also brings in a novel set of challenges, specifically related to the cost of ownership of which backhaul is of primary concern. The radio-frequency (RF) backhaul provides a scalable and easy to plan and deploy solution, its performance, however, is limited by latency problems. FSO backhaul offers a better overall latency and a higher data rate than the RF backhauls, however, the performance of FSO is sensitive to weather conditions. To combine the advantages of RF backhauls, and FSO backhauls, this paper proposes a cost-efficient backhaul network using the hybrid RF/FSO technology. To ensure a resilient backhaul, a given degree of redundancy is guaranteed to cope with link failure by connecting each node through $K$ link-disjoint paths. Hence, the network planning problem is the one of minimizing the total deployment cost by choosing the appropriate link type, i.e., either hybrid RF/FSO or optical fiber, between each couple of base-stations while guaranteeing $K$ link-disjoint connections, a targeted data rate, and a reliability threshold. Given the complexity of finding the optimal solution, this paper suggests reformulating the problem as a more tractable maximum weight clique problem in the planning graph under a specified realistic assumption about the cost of OF and hybrid RF/FSO links. Simulation results show the cost of the different planning and suggest that the proposed heuristic solution have a close-to-optimal performance for a significant gain in computation complexity.
    Preview · Article · Aug 2015
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    Alam Zaib · Mudassir Masood · Anum Ali · Weiyu Xu · Tareq Y. Al-Naffouri
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    ABSTRACT: Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed linear minimum mean square error (LMMSE) algorithm that can achieve near optimal channel estimates at very low complexity by exploiting the strong spatial correlations and symmetry of large antenna array elements. The proposed method involves solving a (fixed) reduced dimensional LMMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring antenna elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. We also analyse the effect of pilot contamination on the mean square error (MSE) performance of different channel estimation techniques. Unlike the conventional approaches, we use stochastic geometry to obtain analytical expression for interference variance (or power) across OFDM frequency tones and use it to derive the MSE expressions for different algorithms under both noise and pilot contaminated regimes. Simulation results validate our analysis and the near optimal MSE performance of proposed estimation algorithms.
    Full-text · Article · Jul 2015
  • L.H. Afify · H. ElSawy · T.Y. Al-Naffouri · M.-S. Alouini
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    ABSTRACT: Modeling aggregate network interference in cellular networks has recently gained immense attention both in academia and industry. While stochastic geometry based models have succeeded to account for the cellular network geometry, they mostly abstract many important wireless communication system aspects (e.g., modulation techniques, signal recovery techniques). Recently, a novel stochastic geometry model, based on the Equivalent-in-Distribution (EiD) approach, succeeded to capture the aforementioned communication system aspects and extend the analysis to averaged error performance, however, on the expense of increasing the modeling complexity. Inspired by the EiD approach, the analysis developed in [1] takes into consideration the key system parameters, while providing a simple tractable analysis. In this paper, we extend this framework to study the effect of different interference management techniques in downlink cellular network. The accuracy of the proposed analysis is verified via Monte Carlo simulations.
    No preview · Article · Jul 2015
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    Khalil Elkhalil · Abla Kammoun · Tareq Y Al-Naffouri · Mohamed-Slim Alouini
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    ABSTRACT: This paper addresses the development of analytical tools for the computation of the moments of random Gram matrices with one side correlation. Such a question is mainly driven by applications in signal processing and wireless communications wherein such matrices naturally arise. In particular, we derive closed-form expressions for the inverse moments and show that the obtained results can help approximate several performance metrics such as the average estimation error corresponding to the Best Linear Unbiased Estimator (BLUE) and the Linear Minimum Mean Square Error LMMSE or also other loss functions used to measure the accuracy of covariance matrix estimates.
    Full-text · Article · Jul 2015
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    ABSTRACT: This paper presents a novel narrowband interference (NBI) mitigation scheme for SC-FDMA systems. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data-free to sense the NBI signal at the receiver. Further, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between NBI sources and the system under consideration. Towards this end, first, we present an accurate grid mismatch model that is capable of assuming independent offsets for multiple NBI sources. Secondly, prior to NBI reconstruction, we restore the sparsity of the unknown signal by employing a sparsifying transform. To improve the spectral efficiency of the proposed scheme, we outline a data-aided NBI recovery procedure that relies on adaptively selecting a subset of data-points and using them as additional measurements to enhance the NBI estimation. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.
    Full-text · Article · Jul 2015
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    ABSTRACT: Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays in order to make a selection decision. This requirement poses two important challenges which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback.
    No preview · Article · Jul 2015 · IEEE Transactions on Communications
  • Hesham Elsawy · Hayssam Dahrouj · Tareq Y. Al-naffouri · Mohamed-slim Alouini
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    ABSTRACT: Cellular networks have preserved an application agnostic and base station (BS) centric architecture1 for decades. Network functionalities (e.g. user association) are decided and performed regardless of the underlying application (e.g. automation, tactile Internet, online gaming, multimedia). Such an ossified architecture imposes several hurdles against achieving the ambitious metrics of next generation cellular systems. This article first highlights the features and drawbacks of such architectural ossification. Then the article proposes a virtualized and cognitive network architecture, wherein network functionalities are implemented via software instances in the cloud, and the underlying architecture can adapt to the application of interest as well as to changes in channels and traffic conditions. The adaptation is done in terms of the network topology by manipulating connectivities and steering traffic via different paths, so as to attain the applications??? requirements and network design objectives. The article presents cognitive strategies to implement some of the classical network functionalities, along with their related implementation challenges. The article further presents a case study illustrating the performance improvement of the proposed architecture as compared to conventional cellular networks, both in terms of outage probability and handover rate.
    No preview · Article · Jul 2015 · IEEE Communications Magazine
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    Ebrahim B Al-Safadi · Tareq Y Al-Naffouri · Mudassir Masood · Anum Ali
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    ABSTRACT: A novel method for correcting the effect of nonlinear distortion in orthogonal frequency division multiplexing signals is proposed. The method depends on adaptively selecting the distortion over a subset of the data carriers, and then using tools from compressed sensing and sparse Bayesian recovery to estimate the distortion over the other carriers. Central to this method is the fact that carriers (or tones) are decoded with different levels of confidence, depending on a coupled function of the magnitude and phase of the distortion over each carrier, in addition to the respective channel strength. Moreover, as no pilots are required by this method, a significant improvement in terms of achievable rate can be achieved relative to previous work.
    Full-text · Technical Report · Jun 2015

Publication Stats

1k Citations
140.94 Total Impact Points

Institutions

  • 2007-2016
    • King Fahd University of Petroleum and Minerals
      • Department of Electrical Engineering
      Az̧ Z̧ahrān, Eastern Province, Saudi Arabia
  • 2014
    • University of Victoria
      Victoria, British Columbia, Canada
  • 2013-2014
    • King Abdullah University of Science and Technology
      • Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE)
      Djidda, Makkah, Saudi Arabia
  • 2006
    • Boston University
      • Department of Electrical and Computer Engineering
      Boston, Massachusetts, United States
  • 1998-2005
    • Stanford University
      • Department of Electrical Engineering
      Palo Alto, California, United States
    • Georgia Institute of Technology
      • School of Electrical & Computer Engineering
      Atlanta, GA, United States