KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/660695
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AbstractWe review selected results related to the robustness of networked systems in finite and asymptotically large size regimes in static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small-gain-type analysis provides tight robustness guarantees for linear dynamics over finite networks toward worst-case and stochastic disturbances. We discuss network flow dynamic settings where nonlinear techniques facilitate understanding the effect, on robustness, of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast cascading failure with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through this review, we aim to achieve two objectives: to highlight selected settings in which the role of the interconnectivity structure of a network in its robustness is well understood, and to highlight a few additional settings in which existing system-theoretic tools give tight robustness guarantees and that are also appropriate avenues for future network-theoretic investigations.
CitationSavla, K., Shamma, J. S., & Dahleh, M. A. (2020). Network Effects on the Robustness of Dynamic Systems. Annual Review of Control, Robotics, and Autonomous Systems, 3(1), 115–149. doi:10.1146/annurev-control-091219-012549
SponsorsThis work was supported by National Science Foundation CAREER Electrical, Communications, and Cyber Systems grant 1454729 and by funding from King Abdullah University of Science and Technology. The authors thank Bassam Bamieh for helpful discussions.
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Virtualized cognitive network architecture for 5G cellular networksElsawy, Hesham; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim (IEEE Communications Magazine, Institute of Electrical and Electronics Engineers (IEEE), 2015-07-17) [Article]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.
Collaborative Multi-Layer Network Coding For Hybrid Cellular Cognitive Radio NetworksMoubayed, Abdallah J. (2014-05) [Thesis]
Advisor: Alouini, Mohamed-Slim
Committee members: Al-Naffouri, Tareq Y.; Sultan Salem, Ahmed Kamal; Turkiyyah, GeorgeIn this thesis, as an extension to , we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other’s packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network’s performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network’s interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%. Furthermore, with the use of fractional cooperation, the average recovery overhead is further reduced by around 5% for the primary network and around 10% for the secondary network when a high fractional cooperation probability is used.
Conflict free network coding for distributed storage networksAl-Habob, Ahmed A.; Sorour, Sameh; Aboutorab, Neda; Sadeghi, Parastoo (2015 IEEE International Conference on Communications (ICC), Institute of Electrical and Electronics Engineers (IEEE), 2015-06) [Conference Paper]© 2015 IEEE. In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.