112 research outputs found
Energy-Efficient Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
Performance of ad hoc networks with two-hop relay routing and limited packet lifetime (extended version)
We consider a mobile ad hoc network consisting of three types of nodes (source, destination and relay nodes) and using the two-hop relay routing. This type of routing takes advantage of the mobility and the storage capacity of the nodes, called the relay nodes, in order to route packets between a source and a destination. Packets at relay nodes are assumed to have a limited lifetime in the network. Nodes are moving inside a bounded region according to some random mobility model. Closed-form expressions and asymptotic results when the number of nodes is large are provided for the packet delivery delay and for the energy needed to transmit a packet from the source to its destination. We also introduce and evaluate a variant of the two-hop relay protocol that limits the number of generated copies in the network. Our model is validated through simulations for two mobility models (random waypoint and random direction mobility models), and the performance of the two-hop routing and of the epidemic routing protocols are compared.\ud
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Can we use perfect simulation for non-monotonic Markovian systems ?
International audienceSimulation approaches are alternative methods to estimate the stationary be- havior of stochastic systems by providing samples distributed according to the stationary distribution, even when it is impossible to compute this distribution numerically. Propp and Wilson used a backward coupling to derive a simu- lation algorithm providing perfect sampling (i.e. which distribution is exactly stationary) of the state of discrete time finite Markov chains. Here, we adapt their algorithm by showing that, under mild assumptions, backward coupling can be used over two simulation trajectories only
Automating Large-Scale Simulation and Data Analysis with OMNeT++: Lession Learned and Future Perspectives
Simulation is widely adopted in the study of modern computer networks. In
this context, OMNeT++ provides a set of very effective tools that span from the
definition of the network, to the automation of simulation execution and quick
result representation. However, as network models become more and more complex
to cope with the evolution of network systems, the amount of simulation
factors, the number of simulated nodes and the size of results grow
consequently, leading to simulations with larger scale. In this work, we
perform a critical analysis of the tools provided by OMNeT++ in case of such
large-scale simulations. We then propose a unified and flexible software
architecture to support simulation automation
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Self-Organization in Decentralized Networks: A Trial and Error Learning Approach
International audienceIn this paper, the problem of channel selection and power control is jointly analyzed in the context of multiple-channel clustered ad-hoc networks, i.e., decentralized networks in which radio devices are arranged into groups (clusters) and each cluster is managed by a central controller (CC). This problem is modeled by game in normal form in which the corresponding utility functions are designed for making some of the Nash equilibria (NE) to coincide with the solutions to a global network optimization problem. In order to ensure that the network operates in the equilibria that are globally optimal, a learning algorithm based on the paradigm of trial and error learning is proposed. These results are presented in the most general form and therefore, they can also be seen as a framework for designing both games and learning algorithms with which decentralized networks can operate at global optimal points using only their available local knowledge. The pertinence of the game design and the learning algorithm are highlighted using speciïŹc scenarios in decentralized clustered ad hoc networks. Numerical results conïŹrm the relevance of using appropriate utility functions and trial and error learning for enhancing the performance of decentralized networks
Exact Worst-case Delay in FIFO-multiplexing Feed-forward Networks
In this paper, we compute the actual worst-case end-to-end delay for a flow in a feed-forward network of first-inâfirst-out (FIFO)-multiplexing service curve nodes, where flows are shaped by piecewise-affine concave arrival curves, and service curves are piecewise affine and convex. We show that the worst-case delay problem can be formulated as a mixed integer linear programming problem, whose size grows exponentially with the number of nodes involved. Furthermore, we present approximate solution schemes to find upper and lower delay bounds on the worst-case delay. Both only require to solve just one linear programming problem and yield bounds that are generally more accurate than those found in the previous work, which are computed under more restrictive assumptions
Can we use perfect simulation for non-monotonic Markovian systems ?
International audienceSimulation approaches are alternative methods to estimate the stationary be- havior of stochastic systems by providing samples distributed according to the stationary distribution, even when it is impossible to compute this distribution numerically. Propp and Wilson used a backward coupling to derive a simu- lation algorithm providing perfect sampling (i.e. which distribution is exactly stationary) of the state of discrete time finite Markov chains. Here, we adapt their algorithm by showing that, under mild assumptions, backward coupling can be used over two simulation trajectories only
Automating Large-Scale Simulation and Data Analysis with OMNeT++: Lession Learned and Future Perspectives
Simulation is widely adopted in the study of modern computer networks. In
this context, OMNeT++ provides a set of very effective tools that span from the
definition of the network, to the automation of simulation execution and quick
result representation. However, as network models become more and more complex
to cope with the evolution of network systems, the amount of simulation
factors, the number of simulated nodes and the size of results grow
consequently, leading to simulations with larger scale. In this work, we
perform a critical analysis of the tools provided by OMNeT++ in case of such
large-scale simulations. We then propose a unified and flexible software
architecture to support simulation automation
Perfect Simulation and Non-monotone Markovian Systems
International audiencePerfect simulation, or coupling from the past, is an efficient technique for sampling the steady state of monotone discrete time Markov chains. Indeed, one only needs to consider two trajectories corresponding to minimal and maximal state in the system. We show here that even for non-monotone systems one only needs to compute two trajectories: an infimum and supremum envelope. Since the sequence of states obtained by taking infimum (resp. supremum) at each time step does not correspond to a feasible trajectory of the system, envelopes and not feasible trajectories. We show that the envelope approach is efficient for some classes of non-monotone queuing networks, such as networks of queues with batch arrivals, queues with fork and join nodes and/or with negative customers
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