6 research outputs found

    Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services

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    This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book

    Computing Shapley Values in the Plane

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    We consider the problem of computing Shapley values for points in the plane, where each point is interpreted as a player, and the value of a coalition is defined by the area of usual geometric objects, such as the convex hull or the minimum axis-parallel bounding box. For sets of n points in the plane, we show how to compute in roughly O(n^{3/2}) time the Shapley values for the area of the minimum axis-parallel bounding box and the area of the union of the rectangles spanned by the origin and the input points. When the points form an increasing or decreasing chain, the running time can be improved to near-linear. In all these cases, we use linearity of the Shapley values and algebraic methods. We also show that Shapley values for the area of the convex hull or the minimum enclosing disk can be computed in O(n^2) and O(n^3) time, respectively. These problems are closely related to the model of stochastic point sets considered in computational geometry, but here we have to consider random insertion orders of the points instead of a probabilistic existence of points

    Deterministic Digital Clustering of Wireless Ad Hoc Networks

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    We consider deterministic distributed communication in wireless ad hoc networks of identical weak devices under the SINR model without predefined infrastructure. Most algorithmic results in this model rely on various additional features or capabilities, e.g., randomization, access to geographic coordinates, power control, carrier sensing with various precision of measurements, and/or interference cancellation. We study a pure scenario, when no such properties are available. As a general tool, we develop a deterministic distributed clustering algorithm. Our solution relies on a new type of combinatorial structures (selectors), which might be of independent interest. Using the clustering, we develop a deterministic distributed local broadcast algorithm accomplishing this task in O(ΔlogNlogN)O(\Delta \log^*N \log N) rounds, where Δ\Delta is the density of the network. To the best of our knowledge, this is the first solution in pure scenario which is only polylog(n)(n) away from the universal lower bound Ω(Δ)\Omega(\Delta), valid also for scenarios with randomization and other features. Therefore, none of these features substantially helps in performing the local broadcast task. Using clustering, we also build a deterministic global broadcast algorithm that terminates within O(D(Δ+logN)logN)O(D(\Delta + \log^* N) \log N) rounds, where DD is the diameter of the network. This result is complemented by a lower bound Ω(DΔ11/α)\Omega(D \Delta^{1-1/\alpha}), where α>2\alpha > 2 is the path-loss parameter of the environment. This lower bound shows that randomization or knowledge of own location substantially help (by a factor polynomial in Δ\Delta) in the global broadcast. Therefore, unlike in the case of local broadcast, some additional model features may help in global broadcast

    Towards reliable geographic broadcasting in vehicular networks

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    In Vehicular ad hoc Networks (VANETs), safety-related messages are broadcasted amongst cars, helping to improve drivers' awareness of the road situation. VANETs’ reliability are highly affected by channel contention. This thesis first addresses the issue of channel use efficiency in geographical broadcasts (geocasts). Constant connectivity changes inside a VANET make the existing routing algorithms unsuitable. This thesis presents a geocast algorithm that uses a metric to estimate the ratio of useful to useless packet received. Simulations showed that this algorithm is more channel-efficient than the farthest-first strategy. It also exposes a parameter, allowing it to adapt to channel load. Second, this thesis presents a method of estimating channel load for providing feedback to moderate the offered load. A theoretical model showing the relationship between channel load and the idle time between transmissions is presented and used to estimate channel contention. Unsaturated stations on the network were shown to have small but observable effects on this relationship. In simulations, channel estimators based on this model show higher accuracy and faster convergence time than by observing packet collisions. These estimators are also less affected by unsaturated stations than by observing packet collisions. Third, this thesis couples the channel estimator to the geocast algorithm, producing a closed-loop load-reactive system that allows geocasts to adapt to instantaneous channel conditions. Simulations showed that this system is not only shown to be more efficient in channel use and be able to adapt to channel contention, but is also able to self-correct suboptimal retransmission decisions. Finally, this thesis demonstrates that all tested network simulators exhibit unexpected behaviours when simulating broadcasts. This thesis describes in depth the error in ns-3, leading to a set of workarounds that allows results from most versions of ns-3 to be interpreted correctly
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