Tareq Y. Al-Naffouri

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

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Publications (164)146.9 Total impact

  • Ahmed Douik · Sameh Sorour · Tareq Y. Al-Naffouri · M.-S. Alouini
    [Show abstract] [Hide abstract] 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
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    Khalil Elkhalil · Abla Kammoun · Tareq Y Al-Naffouri · Mohamed-Slim Alouini
    [Show abstract] [Hide abstract] ABSTRACT: In this paper, we derive a closed-form expression for the inverse moments of one sided-correlated random Gram matrices. Such a question is mainly motivated by applications in signal processing and wireless communications for which evaluating this quantity is a question of major interest. This is for instance the case of the best linear unbiased estimator, in which the average estimation error corresponds to the first inverse moment of a random Gram matrix.
    Full-text · Conference Paper · Jul 2016
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    Full-text · Conference Paper · Jul 2016
  • Rabe Arshad · Hesham ElSawy · Sameh Sorour · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
    [Show abstract] [Hide abstract] ABSTRACT: Cellular operators are continuously densifying their networks to cope with the ever-increasing capacity demand. Furthermore, an extreme densification phase for cellular networks is foreseen to fulfill the ambitious fifth generation (5G) performance requirements. Network densification improves spectrum utilization and network capacity by shrinking base stations' (BSs) footprints and reusing the same spectrum more frequently over the spatial domain. However, network densification also increases the handover (HO) rate, which may diminish the capacity gains for mobile users due to HO delays. In highly dense 5G cellular networks, HO delays may neutralize or even negate the gains offered by network densification. In this paper, we present an analytical paradigm, based on stochastic geometry, to quantify the effect of HO delay on the average user rate in cellular networks. To this end, we propose a flexible handover scheme to reduce HO delay in case of highly dense cellular networks. This scheme allows skipping the HO procedure with some BSs along users' trajectories. The performance evaluation and testing of this scheme for only single HO skipping shows considerable gains in many practical scenarios.
    No preview · Article · Apr 2016
  • Laila Hesham Afify · Hesham ElSawy · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
    [Show abstract] [Hide abstract] ABSTRACT: This paper presents a unified mathematical paradigm, based on stochastic geometry, for downlink cellular networks with multiple-input-multiple-output (MIMO) base stations (BSs). The developed paradigm accounts for signal retransmission upon decoding errors, in which the temporal correlation among the signal-to-interference plus-noise-ratio (SINR) of the original and retransmitted signals is captured. In addition to modeling the effect of retransmission on the network performance, the developed mathematical model presents twofold analysis unification for MIMO cellular networks literature. First, it integrates the tangible decoding error probability and the abstracted (i.e., modulation scheme and receiver type agnostic) outage probability analysis, which are largely disjoint in the literature. Second, it unifies the analysis for different MIMO configurations. The unified MIMO analysis is achieved by abstracting unnecessary information conveyed within the interfering signals by Gaussian signaling approximation along with an equivalent SISO representation for the per-data stream SINR in MIMO cellular networks. We show that the proposed unification simplifies the analysis without sacrificing the model accuracy. To this end, we discuss the diversity-multiplexing tradeoff imposed by different MIMO schemes and shed light on the diversity loss due to the temporal correlation among the SINRs of the original and retransmitted signals. Finally, several design insights are highlighted.
    No preview · Article · Apr 2016
  • F. Sana · K. Katterbauer · T. Al-Naffouri · I. Hoteit
    [Show abstract] [Hide abstract] ABSTRACT: Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.
    No preview · Article · Apr 2016
  • No preview · Article · Jan 2016 · IEEE Transactions on Communications
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    Full-text · Article · Jan 2016 · IEEE Transactions on Vehicular Technology
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    [Show abstract] [Hide abstract] 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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    Oualid Hammi · Abubaker Abdelhafiz · Tareq Y. Al-Naffouri · Fadhel M. Ghannouchi
    Full-text · Conference Paper · Dec 2015
  • Laila Afify · Hesham Elsawy · Tareq Al-Naffouri · Mohamed-Slim Alouini
    [Show abstract] [Hide abstract] ABSTRACT: Stochastic geometry analysis for cellular networks is mostly limited to outage probability and ergodic rate, which abstract many important wireless communication aspects. Recently, a novel technique based on the Equivalent-in-Distribution (EiD) approach is proposed to extend the analysis to capture these metrics and analyze bit error probability (BEP) and symbol error probability (SEP). However, the EiD approach considerably increases the complexity of the analysis. In this letter, we propose an approximate yet accurate framework, that is also able to capture fine wireless communication details similar to the EiD approach, but with simpler analysis. The proposed methodology is verified against the exact EiD analysis in both downlink and uplink cellular networks scenarios.
    No preview · Article · Dec 2015 · IEEE Communications Letters
  • Mohammed E. Eltayeb · Ahmed Alkhateeb · Robert W. Heath · Tareq Y. Al-Naffouri
    No preview · Conference Paper · Dec 2015
  • Ahmed Douik · Sameh Sorour · Tareq Y. Al-Naffouri · Mohamed-Slim Alouini
    [Show abstract] [Hide abstract] 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
    [Show description] [Hide description] 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
    [Show abstract] [Hide abstract] 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
    [Show abstract] [Hide abstract] 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
  • Mudassir Masood · Laila H. Afify · Tareq Y. Al-Naffouri
    [Show abstract] [Hide abstract] 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
    [Show description] [Hide description] 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

Publication Stats

1k Citations
146.90 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
  • 1998-2004
    • Stanford University
      • Department of Electrical Engineering
      Stanford, CA, United States
    • Georgia Institute of Technology
      • School of Electrical & Computer Engineering
      Atlanta, GA, United States