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  • 1.
    Athanasiou, George
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Association control in millimeterWave wireless access networks2014In: 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2014, 2014, p. 260-264Conference paper (Refereed)
    Abstract [en]

    The resource allocation problem of optimal assignment of the stations to the available access points in 60 GHz millimeterWave wireless access networks is investigated. The problem is posed as a multi-assignment optimization problem. The proposed solution method converts the initial problem to a minimum cost flow problem and allows to design an efficient algorithm by a combination of auction algorithms. The solution algorithm exploits the network optimization structure of the problem, and thus is much more powerful than computationally intensive general-purpose solvers. Theoretical and numerical results evince numerous properties, such as optimality, convergence, and scalability in comparison to existing approaches.

  • 2.
    Athanasiou, George
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Orten, P.
    Communication infrastructures in industrial automation: The case of 60 GHz millimeterWave communications2013In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2013Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks for industrial automation applications must offer timely, reliable, and energy efficient communications at both low and high data rate. While traditional communication technologies between 2.4 GHz and 5 GHz are sometimes incapable to efficiently achieve the aforementioned goals, new communication strategies are emerging, such as millimeterWave communications. In this overview paper, the general requirements that factory and process automation impose on the network design are reviewed. Moreover, this paper presents and qualitatively evaluates the 60 GHz millimeterWave communication technology for automation. It is argued that the upcoming 60 GHz millimeterWave technology brings an enormous potential and can influence the design of the future communication infrastructures in factory and process automation.

  • 3.
    Athanasiou, Georgios
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Auction-Based Resource Allocation in MillimeterWave Wireless Access Networks2013In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 17, no 11, p. 2108-2111Article in journal (Refereed)
    Abstract [en]

    The resource allocation problem of optimal assignment of the stations to the available access points in 60 GHz millimeterWave wireless access networks is investigated. The problem is posed as a multi-assignment optimization problem. The proposed solution method converts the initial problem to a minimum cost flow problem and allows to design an efficient algorithm by a combination of auction algorithms. The solution algorithm exploits the network optimization structure of the problem, and thus is much more powerful than computationally intensive general-purpose solvers. Theoretical and numerical results evince numerous properties, such as optimality, convergence, and scalability in comparison to existing approaches.

  • 4.
    Athanasiou, Georgios
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Tassiulas, Leandros
    University of Thessaly, Volos, Greece.
    Optimizing Client Association for Load Balancing and Fairness in Millimeter Wave Wireless Networks2015In: IEEE/ACM Transactions on Networking, ISSN 1063-6692, E-ISSN 1558-2566, Vol. 23, no 3, p. 836-850Article in journal (Refereed)
    Abstract [en]

    Millimeter-wave communications in the 60-GHz band are considered one of the key technologies for enabling multigigabit wireless access. However, the special characteristics of such a band pose major obstacles to the optimal utilization of the wireless resources, where the problem of efficient client association to access points (APs) is of vital importance. In this paper, the client association in 60-GHz wireless access networks is investigated. The AP utilization and the quality of the rapidly vanishing communication links are the control parameters. Because of the tricky non-convex and combinatorial nature of the client association optimization problem, a novel solution method is developed to guarantee balanced and fair resource allocation. A new distributed, lightweight, and easy-to-implement association algorithm, based on Lagrangian duality theory and subgradient methods, is proposed. It is shown that the algorithm is asymptotically optimal, that is, the relative duality gap diminishes to zero as the number of clients increases.

  • 5. Joshi, Satya Krishna
    et al.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Codreanu, Marian
    Latva-aho, Matti
    Weighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 4, p. 2090-2095Article in journal (Refereed)
    Abstract [en]

    The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

  • 6. Levorato, M.
    et al.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed optimization of transmission strategies in reactive cognitive networks2014In: 2014 IEEE Global Communications Conference, GLOBECOM 2014, 2014, p. 905-910Conference paper (Refereed)
    Abstract [en]

    A framework for the distributed optimization of reactive cognitive networks with multiple secondary users is presented. The secondary users iteratively locate the policy maximizing their aggregate performance under bounds on the maximum performance loss caused to the primary users. The policy accounts for the impact of interference on the dynamics of the primary users' network due to transmission and channel access protocols. To minimize coordination overhead, it is assumed that the secondary users only coordinate the policy, whereas actions in each slot are independently selected by the individual secondary user based on the agreed policy. The distributed optimization technique proposed herein is based on alternating convex optimization. Numerical results are presented assessing the performance of the obtained transmission policy with respect to the optimal centralized and fully-coordinated policy.

  • 7.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Chathuranga Weeraddana, Pradeep
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Distributed Approach for the Optimal Power Flow Problem Based on ADMM and Sequential Convex Approximations2015In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 2, no 3, p. 238-253Article in journal (Refereed)
    Abstract [en]

    The optimal power flow (OPF) problem, which playsa central role in operating electrical networks is considered. Theproblem is nonconvex and is in fact NP hard. Therefore, designingefficient algorithms of practical relevance is crucial, thoughtheir global optimality is not guaranteed. Existing semi-definiteprogramming relaxation based approaches are restricted to OPFproblems where zero duality holds. In this paper, an efficientnovel method to address the general nonconvex OPF problemis investigated. The proposed method is based on alternatingdirection method of multipliers combined with sequential convexapproximations. The global OPF problem is decomposed intosmaller problems associated to each bus of the network, thesolutions of which are coordinated via a light communicationprotocol. Therefore, the proposed method is highly scalable. Theconvergence properties of the proposed algorithm are mathematicallysubstantiated. Finally, the proposed algorithm is evaluatedon a number of test examples, where the convergence propertiesof the proposed algorithm are numerically substantiated and theperformance is compared with a global optimal method.

  • 8.
    Magnússon, Sindri
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Rabbat, Michael
    McGill University, Department of Electrical and Computer Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On the Convergence of an Alternating Direction Penalty Method for Nonconvex Problems2015In: Signals, Systems and Computers, 2015 Asilomar Conference on, IEEE Computer Society, 2015Conference paper (Other academic)
    Abstract [en]

    This paper investigates convergence properties ofscalable algorithms for nonconvex and structured optimization.We consider a method that is adapted from the classic quadraticpenalty function method, the Alternating Direction PenaltyMethod (ADPM). Unlike the original quadratic penalty functionmethod, in which single-step optimizations are adopted, ADPMuses alternating optimization, which in turn is exploited toenable scalability of the algorithm. A special case of ADPM isa variant of the well known Alternating Direction Method ofMultipliers (ADMM), where the penalty parameter is increasedto infinity. We show that due to the increasing penalty, theADPM asymptotically reaches a primal feasible point undermild conditions. Moreover, we give numerical evidence thatdemonstrates the potential of the ADPM for computing localoptimal points when the penalty is not updated too aggressively.

  • 9.
    Weeraddana, P. Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Codreanu, M.
    Latva-Aho, M.
    Ephremides, A.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weighted sum-rate maximization in wireless networks: A review2011In: Foundations and Trends in Networking, ISSN 1554-057X, Vol. 6, no 1-2, p. 1-163Article, review/survey (Refereed)
    Abstract [en]

    A wide variety of resource management problems of recent interest, including power/rate control, link scheduling, cross-layer control, network utility maximization, beamformer design of multiple-input multiple-output networks, and many others are directly or indirectly reliant on the weighted sum-rate maximization (WSRMax) problem. In general, this problem is very difficult to solve and is NP-hard. In this review, we provide a cohesive discussion of the existing solution methods associated with the WSRMax problem, including global, fast local, as well as decentralized methods. We also discuss in depth the applications of general optimization techniques, such as branch and bound methods, homotopy methods, complementary geometric programming, primal decomposition methods, subgradient methods, and sequential approximation strategies, in order to develop algorithms for the WSRMax problem. We show, through a number of numerical examples, the applicability of these algorithms in various application domains.

  • 10.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Athanasiou, Georgios
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Baras, John S.
    University of Maryland.
    Per-se Privacy Preserving Solution Methods Based on Optimization2013In: Per-se Privacy Preserving Solution Methods Based on Optimization, IEEE conference proceedings, 2013Conference paper (Refereed)
    Abstract [en]

    Ensuring privacy is an essential requirement in various contexts, such as social networks, healthcare data, ecommerce, banks, and government services. Here, different entities coordinate to address specific problems where the sensitive problem data are distributed among the involved entities and no entity wants to publish its data during the solution procedure. Existing privacy preserving solution methods are mostly based on cryptographic procedures and thus have the drawback of substantial computational complexity. Surprisingly, little attention has been devoted thus far to exploit mathematical optimization techniques and their inherent properties for preserving privacy. Yet, optimization based approaches to privacy require much less computational effort compared to cryptographic variants, which is certainly desirable in practice. In this paper, a unified framework for transformation based optimization methods that ensure privacy is developed. A general definition for the privacy in the context of transformation methods is proposed. A number of examples are provided to illustrate the ideas. It is concluded that the theory is still in its infancy and that huge benefits can be achieved by a substantial development.

  • 11.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Athanasiou, Georgios
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Jakobsson, Martin
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Baras, John S.
    University of Maryland.
    Per-se Privacy Preserving Distributed OptimizationIn: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523Article in journal (Other academic)
    Abstract [en]

    Ensuring privacy in distributed optimization is essential in many contexts, such as healthcare data,banks, e-commerce, government services, and social networks. In these contexts, it is common thatdifferent parties coordinate to solve a specific optimization problem whose data is dispersed amongthe parties, where no entity wants to publish its data during the solution procedure. Addressing theseproblems falls under the umbrella of well-knownsecured multiparty computation(SMC). Existingapproaches for SMC are mostly based on cryptography. Surprisingly, little attention has been devotedthus far to develop non-cryptographic approaches, that can be much more efficient. In this paper,we investigate alternative non-cryptographic methods based onmathematical optimization techniques.First, aunified frameworkto encapsulate existing non-cryptographic methods, which rely algebraictransformations to disguise sensitive problem data, is developed. The proposed framework capitalizes onkey optimization techniques, such aschange of variablesandtransformation of objective and constraintfunctions, for equivalent problem formation. Moreover, the privacy preserving properties that are inherentin the mathematical optimization techniques, including classical decomposition methods (e.g., primal anddual decomposition), and state-of-the-art methods, such as alternating direction method of multipliersare investigated. A general definition for quantifying the privacy in the context of non-cryptographicapproaches is proposed. A number of examples are provided to illustrate the importance of our proposedalgorithms. It is concluded that the theory is in its infancy and that huge benefits can be achieved by asubstantial development.

  • 12.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Codreanu, M.
    Latva-Aho, M.
    Ephremides, A.
    Multicell MISO downlink weighted sum-rate maximization: A distributed approach2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 556-570Article in journal (Refereed)
    Abstract [en]

    We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g., fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.

  • 13.
    Weeraddana, Pradeep Chathuranga
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Codreanu, Marian
    University of Oulu, Finland.
    Latva-aho, Matti
    University of Oulu, Finland.
    Ephremides, Anthony
    University of Maryland.
    Multicell Downlink Weighted Sum-Rate Maximization: A Distributed Approach2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 556-570Article in journal (Refereed)
    Abstract [en]

    We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g. fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.

  • 14.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Weeraddana, Pradeep Chathuranga
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    A Semi Distributed Approach for Min-Max Fair Agent-assignment Problem with Privacy GuaranteesArticle in journal (Refereed)
1 - 14 of 14
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