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  • 1. Boem, F.
    et al.
    Xu, Yuzhe
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parisini, T.
    A distributed pareto-optimal dynamic estimation method2015In: 2015 European Control Conference, ECC 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 3673-3680Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel distributed model-based prediction method is proposed using sensor networks. Each sensor communicates with the neighboring nodes for state estimation based on a consensus protocol without centralized coordination. The proposed distributed estimator consists of a consensus-filtering scheme, which uses a weighted combination of sensors information, and a model-based predictor. Both the consensus-filtering weights and the model-based prediction parameter for all the state components are jointly optimized to minimize the variance and bias of the prediction error in a Pareto framework. It is assumed that the weights of the consensus-filtering phase are unequal for the different state components, unlike consensus-based approaches from literature. The state, the measurements, and the noise components are assumed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. The optimal weights are derived and it is established that the consensus-filtering weights and the model-based prediction parameters cannot be designed separately in an optimal way. The asymptotic convergence of the mean of the prediction error is demonstrated. Simulation results show the performance of the proposed method, obtaining better results than distributed Kalman filtering. © 2015 EUCA.

  • 2.
    Boem, Francesca
    et al.
    Department of Industrial and Information Engineering, University of Trieste.
    Xu, Yuzhe
    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.
    Parisini, Thomas
    Department of Industrial and Information Engineering, University of Trieste.
    A distributed estimation method for sensor networks based on Pareto optimization2012In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE , 2012, p. 775-781Conference paper (Refereed)
    Abstract [en]

    A novel distributed estimation method for sensor networks is proposed. The goal is to track a time-varying signal that is jointly measured by a network of sensor nodes despite the presence of noise: each node computes its local estimate as a weighted sum of its own and its neighbors' measurements and estimates and updates its weights to minimize both the variance and the mean of the estimation error by means of a suitable Pareto optimization problem. The estimator does not rely on a central coordination: both parameter optimization and estimation are distributed across the nodes. The performance of the distributed estimator is investigated in terms of estimation bias and estimation error. Moreover, an upper bound of the bias is provided. The effectiveness of the proposed estimator is illustrated via computer simulations and the performances are compared with other distributed schemes previously proposed in the literature. The results show that the estimation quality is comparable to that of one of the best existing distributed estimation algorithms, guaranteeing lower computational cost and time.

  • 3.
    Boem, Francesca
    et al.
    Department of Industrial and Information Engineering, University of Trieste.
    Xu, Yuzhe
    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.
    Parisini, Thomas
    Department of Industrial and Information Engineering, University of Trieste.
    Distributed Fault Detection using Sensor Networks and Pareto Estimation2013In: 2013 European Control Conference, ECC 2013, IEEE conference proceedings, 2013, p. 932-937Conference paper (Refereed)
    Abstract [en]

    In this paper, a preliminary novel distributed fault detection architecture for dynamic systems using sensor networks and a distributed estimation method based on Pareto optimization is proposed. The goal is to monitor large-scale or distributed systems by using a sensor network where each node acts as a local estimation agent without centralized coordination. Probabilistic detection thresholds related to a given rate of false alarms are derived in several different scenarios as far as the measurement pattern and the nominal dynamics is concerned. Preliminary simulation results show the effectiveness of the proposed fault detection methodology.

  • 4.
    Shokri-Ghadikolaei, Hossein
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Xu, Yuzhe
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    User association and the alignment-throughput tradeoff in millimeter wave networks2015In: Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2015 IEEE 1st International Forum on, IEEE Communications Society, 2015, p. 100-105Conference paper (Refereed)
    Abstract [en]

    Millimeter wave (mmWave) communication is apromising candidate for future extremely high data rate, wirelessnetworks. The main challenges of mmWave communications aredeafness (misalignment between the beams of the transmitterand receiver) and blockage (severe attenuation due to obstacles).Due to deafness, prior to link establishment between a clientand its access point, a time consuming alignment/beam trainingprocedure is necessary, whose complexity depends on the operatingbeamwidth. Addressing blockage may require a reassociationto non-blocked access points, which in turn imposes additionalalignment overhead. This paper introduces a unifying frameworkto maximize network throughput considering both deafness andblockage. A distributed auction-based solution is proposed, wherethe clients and access points act asynchronously to achieveoptimal association along with the optimal operating beamwidth.It is shown that the proposed algorithm provably converges toa solution that maximizes the aggregate network utility withina desired bound. Convergence time and performance boundsare derived in closed-forms. Numerical results confirm superiorthroughput performance of the proposed solution compared toexisting approaches, and highlight the existence of a tradeoffbetween alignment overhead and achievable throughput thataffects the optimal association.

  • 5.
    Weimer, James
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Xu, Yuzhe
    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.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ljungberg, Per
    Donovan, Craig
    Sutor, Ariane
    Fahlén, Lennart E.
    SICS.
    A virtual laboratory for micro-grid information and communication infrastructures2012In: 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), 2012, IEEE conference proceedings, 2012, p. 1-6Conference paper (Refereed)
    Abstract [en]

    Testing smart grid information and communication (ICT) infrastructures is imperative to ensure that they meet industry requirements and standards and do not compromise the grid reliability. Within the micro-grid, this requires identifying and testing ICT infrastructures for communication between distributed energy resources, building, substations, etc. To evaluate various ICT infrastructures for micro-grid deployment, this work introduces the Virtual Micro-Grid Laboratory (VMGL) and provides a preliminary analysis of Long-Term Evolution (LTE) as a micro-grid communication infrastructure.

  • 6.
    Xu, Yuzhe
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Decentralized Network Optimization in Wireless Networks2014Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Distributed estimation and distributed resource allocation are two important services in future cyber-physical wireless networks. The former aims to estimate, or track, physical variables of a phenomenon monitored by a wireless network, whereas the latter aims to optimally assign the limited communication resources to the nodes in a wireless network. These two have one common background theory: optimization problems that are in general nonlinear, non-convex, mixed integer, and need to be solved by distributed algorithms over networks. In this thesis, we report the work from three article submissions, where these distributed optimization problems are considered. The first class of distributed optimization problems that we consider in this thesis is studied in the context of distributed estimation. Here, the design methods and fundamental performance analysis of an adaptive peer-to-peer estimator are established for networks exhibiting message losses. Based on a signal state model, estimates are locally computed at each node of the network by adaptively filtering neighboring nodes’ estimates and measurements communicated over lossy channels. The computation is based on a distributed optimization approach that guarantees the stability of the estimator while minimizing the estimation error variance. The fundamental performance limitations of the estimator are established based on the variance of the estimation error in relation to the message loss process. A non-convex distributed optimization problem with mixed integer and real variables is considered for a resource allocation scenario in a cognitive radio network. In hierarchical cognitive radio networks, unlicensed secondary users can maximize the achievable rates by cooperating with licensed primary users. A maximization of the secondary users achievable rates is proposed by controlling the transmit radio power, the secondary users relaying selection, and power splitting of the relays while guaranteeing primary users performance. Centralized and distributed methods are developed to find the solution to such a challenging mixed integer and non-convex problem. The methods provide a centralized and a distributed algorithm for finding the optimal power allocations for secondary users, and a sub-optimal centralized algorithm and a greedy distributed algorithm for finding the associations between primary and secondary users. Lastly, a distributed binary optimization problem is considered for a millimeterWave wireless access network. At the access level the typical rapidly fading behavior of the millimeterWave channel imposes the careful design of node association to access points, or relaying to other nodes. This challenge is addressed by a distributed approach that optimally solves the joint node association and relaying problem. The problem is posed as a novel multi-assignment optimization problem, for which an original solution method is established by a series of transformations that lead to a tractable minimum cost flow problem. The method allows to design distributed auction solution algorithms where the nodes and relays act asynchronously to achieve optimal node-relay-access point association. It is shown that computational complexity of the new algorithms is much better than centralized general-purpose solvers for multi-assignment optimization, and the algorithms converge to a solution that maximizes the total network throughput within a desired bound. It is concluded that, to the best of our knowledge, there is no general distributed approach for solving the non-convex optimization problems with mixed integer and real decision variables that arises in the scenarios studied in this thesis. The long run goal is to establish a general theoretical framework, which will be pursued for the doctoral dissertation.

  • 7.
    Xu, Yuzhe
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Decentralized Resource Sharing and Associationin in Wireless Networks2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In wireless networks, some of the most important supporting functionalities are decentralized resource sharing and association of clients to access points. These functionalities aim to optimally allocate the available wireless transmission resources, for example, time or spectrum channels, the transmit power, access points, and relaying opportunities, all of which are subject to availability constraints. Decentralized resource sharing and association are designed by a common background theory: optimization problems that are non-convex with mixed integer-real variables, and need to be solved by distributed algorithms over the network nodes. The thesis investigates these optimization problems and establishes novel solution methods based on auction and Lagrangian duality theories. The thesis is divided into two parts: in the first part, the theoretical background of the thesis is given, and novel results on distributed auction theory are established to solve a class of optimization problems with integer variables. These theoretical background and novel results are then used in the second part of the thesis, where published or submitted papers are reported. Specifically, novel solution methods for distributed optimization problems with mixed integer-real variables are in- vestigated for resource sharing and association problems in cognitive radio networks, and in millimeter wave networks.

    In the first paper (J1), a non-convex optimization problem with mixed integer and real variables is considered for resource allocation in a cognitive radio network. In this network, unlicensed secondary users can maximize their achievable transmit rates by cooperating with licensed primary users. An optimization problem to maximize the secondary users achievable rates is proposed by controlling the transmit radio power, the secondary users relaying selection, and power splitting of the relays, while guaranteeing primary users performance. A novel distributed solution method is developed to find the solution to such a challenging mixed integer and non-convex problem. The method provides a distributed algorithm for finding the optimal power allocations for secondary users, and a greedy distributed algorithm for finding the associations between primary and secondary users. Optimality and convergence of the solution method are investigated. The numerical results illustrate the performance of the proposed solution methods, and show that they give a near-to-optimal solution.

    In the second paper, the solution methods investigated in the first paper are used for mixed integer-real optimization problems in millimeter wave networks. These networks are emerging to enable extremely high data rates wireless communications. The main limiting factors of millimeter wave networks are communication blockage (due to high penetration loss of the transmit signals) and deafness (misalignment between the antenna’s beams of the transmitter and receiver). To minimize these limiting factors, it is imperative to design efficient association between clients and access points. Therefore, a general optimization framework to maximize network throughput considering both blockage and deafness is proposed. The optimization framework considers static networks (i.e., no client mobility) and investigates a novel distributed auction based solution, where the clients and access points act asynchronously to achieve optimal association along with the optimal beamwidth of the antennas. A convergence proof and optimality of the auction algorithm are analyzed.

    The optimization framework investigated in the second paper was intended for static networks. In networks with dynamic topologies and channel variations, the dynamic appearance of obstacles or of small misalignments between antenna beams of the transmitters and receivers may trigger the reexecution of the association mechanism, which is time consuming and may be inefficient. This challenge is addressed in the third paper by dynamic distributed association techniques that are robust to wireless channel variations and client mobility. The association problem is formulated as a mixed-integer optimization problem aiming to maximize the network throughput with proportional fairness guarantees. This optimization problem is solved by a distributed dual decomposition algorithm, and by a novel dynamic distributed auction algorithm. A distinguishing novel feature of the proposed algorithms is that the resulting optimal association does not have to be re-computed every time the network changes (e.g., due to client mobility). Instead, it is proved that the algorithm continuously adapts to the network variation and is thus capable to continuously track the optimal solutions. It is shown that the proposed algorithm provably converges to a solution that maximizes the aggregate network utility within a desired bound.

    The previous line of research is then extended to the case where, in addition to designing the association clients-access points, also relays nodes are considered. The association of clients to relaying nodes can provide more uniform quality of service by offering robust millimeter waves connection, load balancing, coverage extension, indoor-outdoor coverage, efficient mobility management, and smooth handover operations. The challenges of clientrelay-access point association are addressed in a sequence of two contributions having different optimization goals: the first one considers the throughput maximization, and the second one the load balancing. In the first contribution (presented in the fourth paper of the thesis), a distributed optimization that solves the joint client association and relaying problem is investigated for the throughput maximization. The optimization problem is posed as a novel multi-dimension assignment optimization, for which an original solution method is established by a series of transformations together with a distributed auction solution algorithm. In the second contribution (presented in the fifth paper of the thesis), the joint association and relaying problem is posed as a novel stochastic optimization problem considering the load balancing, and the resource sharing at access points. The problem is solved by a distributed auction algorithm where the clients and relays act asyn- chronously to quickly achieve optimal association. The convergence time and performance bounds of the algorithm are derived in closed-forms. Numerical results quantify the performance enhancements introduced by the relays, and the substantial improvements of the network throughput and fairness among the clients by the proposed association methods compared to existing approaches.

    The common thread in the line of research reported in the papers of the thesis is given by the solution methods of mixed integer optimization problems that must be solved in a distributed manner. The core result of the thesis is a novel distributed approach, based on the auction theory, for the computation of the solution of a class of optimization problems. It is shown that such an approach can work in static and dynamic network topologies and exhibits convergence and optimality guarantees by leveraging the specific assumptions of the problems’ constraints. Future study could extend the proposed distributed auction method for more general optimization problems for association, such as generalized assignment problems.

     

     

     

     

     

  • 8.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Alfonsetti, E.
    Weeraddana, P. C.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Communication Theory.
    A Semi Distributed Approach for Feasible Min-Max Fair Agent-assignment Problem with Privacy Guarantees2016In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. PP, no 99, article id 7565611Article in journal (Refereed)
    Abstract [en]

    In cyberphysical systems, a relevant problem is assigning agents to slots by distributed decisions capable to preserve agent's privacy. For example, in future intelligent transportation systems, city-level coordinators may optimally assign cars (the agents) to parking slots depending on the cars' distance to final destinations so to ensuring social fairness and without disclosing or even using the car's destination information. Unfortunately, these assignment problems are combinatorial, whereas traditional solvers are exponentially complex, are not scalable, and do not ensure privacy of the agents' intended destinations. Moreover, no emphasis is placed to optimise the agents' social benefit. In this paper, the aggregate social benefit of the agents is considered by an agent-slot assignment optimization problem whose objective function is the fairness among the agents. Due to the problem's complexity, the problem is solved by an approximate approach based on Lagrange duality theory that allows to develop an iterative semi-distributed algorithm. It is shown that the proposed algorithm is gracefully scalable compared to centralised methods, and that it preserves privacy in the sense that an eavesdropper will not be able to discover the destination of any agent during the algorithm iterations. Numerical results illustrate the performance and trade-off of the proposed algorithm compared to the ideal optimal assignment and a greedy method.

  • 9.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Athanasiou, George
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Tassiulas, Leandros
    University of Thessaly.
    Distributed Association and Relaying for Throughput Maximization in Millimeter Wave Wireless NetworksManuscript (preprint) (Other academic)
  • 10.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Real Time Scheduling in LTE for Smart Grids2012In: 5th International Symposium on Communications Control and Signal Processing, ISCCSP 2012, IEEE , 2012, p. 1-6Conference paper (Refereed)
    Abstract [en]

    The latest wireless network, 3GPP Long Term Evolution (LTE), is considered to be a promising solution for smart grids because it provides both low latency and large bandwidth. However, LTE was not originally intended for smart grids applications, where data generated by the grid have specific delay requirements that are different from traditional data or voice communications. In this paper, the specific requirements imposed by a smart grids on the LTE communication infrastructure is first determined. The latency offered by the LTE network to smart grids components is investigated and an empirical mathematical model of the distribution of the latency is established. It is shown by experimental results that with the current LTE up-link scheduler, smart grid latency requirements are not always satisfied and that only a limited number of components can be accommodated. To overcome such a deficiency, a new scheduler of the LTE medium access control is proposed for smart grids. The scheduler is based on a mathematical linear optimization problem that considers simultaneously both the smart grid components and common user equipments. An algorithm for the solution to such a problem is derived based on a theoretical analysis. Simulation results based on this new scheduler illustrate the analysis. It is concluded that LTE can be effectively used in smart grids if new schedulers are employed for improving latency.

  • 11.
    Xu, Yuzhe
    et al.
    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.
    Speranzon, Alberto
    United Technologies Research Center.
    Model Based Peer-to-peer Estimation on Wireless Sensor Netowrks2012In: Estimation and Control of Networked Systems: Vol. 3, Part. 3, 2012, p. 19-24Conference paper (Refereed)
    Abstract [en]

    A peer-to-peer estimator computes local estimates at each node by combining the information from neighboring nodes without the need of central coordination. Although more flexible and scalable, peer-to-peer minimum variance estimators are difficult to design because of message losses and lack of network coordination. In this paper, we propose a new peer-to-peer estimator that allows to recover a time-varying scalar signal from measurements corrupted by an unknown non-zero mean independent noise or disturbances. Message losses occurring over the network and absence of central coordination are considered. Novel theoretical solutions are developed by taking advantage of a model of the signal dynamics. The proposed approach simultaneously guarantees a bounded mean value and minimum variance of the estimation error. Simulation results illustrate the performance of the proposed method.

  • 12.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Speranzon, Alberto
    United Technologies Research Center.
    Model Based Peer-to-Peer Estimator Over Wireless Sensor Networks with Lossy Channels2015In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 61, p. 263-273Article in journal (Refereed)
    Abstract [en]

    In this paper, the design methods and fundamental performance analysis of an adaptive peer-to-peer estimator are established for networks exhibiting message losses. Based on a signal state model, estimates are locally computed at each node of the network by adaptively filtering neighboring nodes’ estimates and measurements communicated over lossy channels. The computation is based on a distributed optimization approach that guarantees the stability of the estimator while minimizing the estimation error variance. Fundamental performance limitations of the estimator are established based on the variance of the estimation error in relation to the message loss process. Numerical simulations validate the theoretical analysis and illustrate the performance with respect to other estimators.

  • 13.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gupta, Vijay
    Department of Electrical Engineering, University of Notre Dame.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed Estimation2013In: E-Reference Signal Processing / [ed] R. Chellappa and S. Theodoridis Eds., Elzevir, Elsevier, 2013Chapter in book (Refereed)
  • 14.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hossein, Shokri-Ghadikolaei
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed Association and Relaying in Millimeter Wave NetworksIn: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248Article in journal (Refereed)
  • 15.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hossein, Shokri-Ghadikolaei
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Dynamic Distributed Association with Fairness in Millimeter Wave NetworksManuscript (preprint) (Other academic)
  • 16.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Liping, Wang
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Distributed spectrum leasing via vertical cooperation in spectrum sharing networks2015In: Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM), 2014 9th International Conference on, IEEE conference proceedings, 2015, p. 185-190Conference paper (Refereed)
    Abstract [en]

    In hierarchical cognitive radio networks, unlicensedsecondary users can increase their achievable rates by assistinglicensed primary user transmissions via cooperation. In thispaper, a novel approach to maximize the transmission rates inthe secondary network by optimizing the relay selection, thesecondary transmit powers, and the cooperative relaying powersplitting parameters is proposed. The resulting optimizationproblem is mixed integer and non-convex, which makes it NPhard to find the optimal solutions. Therefore, centralized anddistributed solution methods to find near-to-optimal solutions ofthis challenging problem are proposed. The methods are basedon iteratively solving the secondary relay selection by a greedyapproach, and the optimal power allocation problem by a fixed-point approach together with alternating direction method ofmultipliers. It is established that both centralized and distributedsolution methods always converge. The numerical results illus-trate the performance of the proposed solution methods, andshow that they give a near-to-optimal solution. Moreover, theperformance margins of the primary transmitters that permitthe accommodation of relaying secondary users, still having highachievable transmit rates, are characterized.

  • 17.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Liping, Wang
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Communication Networks.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Distributed spectrum leasing via vertical cooperation in cognitive radio networks2015In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, p. 185-190Article in journal (Refereed)
    Abstract [en]

    In hierarchical cognitive radio networks, unlicensed secondary users can increase their achievable rates by assisting licensed primary user transmissions via cooperation. In this paper, a novel approach to maximize the transmission rates in the secondary network by optimizing the relay selection, the secondary transmit powers, and the cooperative relaying power splitting parameters is proposed. The resulting optimization problem is mixed integer and non-convex, which makes it NP hard to find the optimal solutions. Therefore, centralized and distributed solution methods to find near-to-optimal solutions of this challenging problem are proposed. The methods are based on iteratively solving the secondary relay selection by a greedy approach, and the optimal power allocation problem by a fixed-point approach together with alternating direction method of multipliers. It is established that both centralized and distributed solution methods always converge. The numerical results illustrate how the performance of the proposed solution methods depend on the primary performance margins, and show that they give a near-to-optimal solution in few iterations.

  • 18.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shokri-Ghadikolaei, Hossein
    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.
    Auction-based dynamic distributed association in millimeter wave networks2016In: 2016 IEEE Globecom Workshops, GC Wkshps 2016 - Proceedings, IEEE, 2016, article id 7848844Conference paper (Refereed)
    Abstract [en]

    Special characteristics of millimeter wave (mmWave) systems such as high vulnerability to random obstacles (due to high penetration loss) and mobility (due to directional communications) demand redesigning the existing algorithms or the association between clients and access points. In this paper, we propose a novel dynamic association scheme, based on the distributed auction algorithm, that is robust to variations of the mmWave wireless channel and to mobility of client. In particular, the resulting optimal association solution does not have to be re-computed every time the network changes (e.g., due to mobility). Instead, the algorithm continuously adapt to the network variation and is thus very efficient. Numerical analysis verifies the ability of the proposed algorithms to optimize the association and to maintain optimality in dynamic environments of mmWave networks.

  • 19.
    Xu, Yuzhe
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Shokri-Ghadikolaei, Hossein
    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.
    Distrubuted association and relaying with fairness in millimeter wave networks2016In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 5, no 12, p. 7955-7970Article in journal (Refereed)
    Abstract [en]

    Millimeter wave (mmWave) systems are emerging as an essential technology for enabling extremely high data rate wireless communications. The main limiting factors of mmWave systems are blockage (high penetration loss) and deafness (misalignment between the beams of the transmitter and receiver). To alleviate these problems, it is imperative to incorporate efficient association and relaying between terminals and access points. Unfortunately, the existing association techniques are designed for the traditional interference-limited networks, and thus are highly suboptimal for mmWave communications due to narrow-beam operations and the resulting non-negligible interference-free behavior. This paper introduces a distributed approach that solves the joint association and relaying problem in mmWave networks considering the load balancing at access points. The problem is posed as a novel stochastic optimization problem, which is solved by distributed auction algorithms where the clients and relays act asynchronously to achieve optimal client-relay-access point association. It is shown that the algorithms provably converge to a solution that maximizes the aggregate logarithmic utility within a desired bound. Numerical results allow quantification of the performance enhancements introduced by the relays, and the substantial improvements of the network throughput and fairness among the clients by the proposed association method as compared to standard approaches. It is concluded that mmWave communications with proper association and relaying mechanisms can support extremely high data rates, connection reliability, and fairness among the clients.

  • 20.
    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 - 20 of 20
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