21 research outputs found

    Cognitive Radio Systems

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    Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

    Energy Efficiency of Wireless Access - Impact of Power Amplifiers and Load Variations

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    In order to ensure seamless coverage and sustainable exponential growth of capacity, there is a keen interest in the development and deployment of highly energy efficient wireless systems and solutions. To design an energy efficient network, it is important to consider the facts that the variations in traffic demand in both temporal and spatial domains are significant, and the power consumption of cellular networks is mostly dominated by macro base stations where the power amplifiers (PAs) consume around 55-70 percent of total energy. One of the main challenges lies in coping with this load variations considering that the PAs attain high efficiency only at around the maximum output power level. In this thesis, we propose energy efficient system level solutions for wireless access network that consider the non-ideal efficiency characteristics of the PA and the load variations.  We model and incorporate the PA efficiency in the energy-delay trade-off present in Shannon's channel capacity model in order to investigate the energy saving potential in a wireless access network at the cost of additional flow-level delay. We propose a best response iteration based distributed power control algorithm where the cells identify the power levels for different user locations to minimize energy consumption under delay constraints. We observe that energy saving potential strongly depends on the network load and PA efficiency characteristics. We also investigate the impact of additional delay in the downlink on the energy consumption of the mobile terminal.  Heterogeneous network is the leading technology for the next-generation cellular networks. We investigate the energy-efficient densification and load sharing between the layers of a heterogeneous network while taking into consideration the PA efficiency and temporal load variations (TLV). We also study the impact of PA efficiency and energy-delay trade-off on the energy efficient network densification.  Massive MIMO (MM) is another leading candidate technology to cater for very high capacity demand. We consider a multi-cell MM system and provide the guidelines to dimension the PA for the antennas. We also develop energy efficient antenna adaptation schemes that allow the cells to dynamically adapt the number of antennas to the TLV in order to maintain high energy efficiency (EE) throughout the day. Our results indicate that these proposed antenna adaptation schemes can improve the EE significantly

    Heavy-traffic revenue maximization in parallel multiclass queues

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    Motivated by revenue maximization in server farms with admission control, we investigate the optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define revenue as a weighted sum of class throughputs. Under these assumptions, we describe a heavy-traffic limit for the revenue maximization problem and study the asymptotic properties of the optimization model as the number of clients increases. Our main result is a simple heuristic that is able to provide tight guarantees on the optimality gap of its solutions. In the general case with M queues and R classes, we prove that our heuristic is (1+1M-1)-competitive in heavy-traffic. Experimental results indicate that the proposed heuristic is remarkably accurate, despite its negligible computational costs, both in random instances and using service rates of a web application measured on multiple cloud deployments

    Closed queueing networks under congestion: non-bottleneck independence and bottleneck convergence

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    We analyze the behavior of closed product-form queueing networks when the number of customers grows to infinity and remains proportionate on each route (or class). First, we focus on the stationary behavior and prove the conjecture that the stationary distribution at non-bottleneck queues converges weakly to the stationary distribution of an ergodic, open product-form queueing network. This open network is obtained by replacing bottleneck queues with per-route Poissonian sources whose rates are determined by the solution of a strictly concave optimization problem. Then, we focus on the transient behavior of the network and use fluid limits to prove that the amount of fluid, or customers, on each route eventually concentrates on the bottleneck queues only, and that the long-term proportions of fluid in each route and in each queue solve the dual of the concave optimization problem that determines the throughputs of the previous open network.Comment: 22 page

    Scalable analysis of stochastic process algebra models

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    The performance modelling of large-scale systems using discrete-state approaches is fundamentally hampered by the well-known problem of state-space explosion, which causes exponential growth of the reachable state space as a function of the number of the components which constitute the model. Because they are mapped onto continuous-time Markov chains (CTMCs), models described in the stochastic process algebra PEPA are no exception. This thesis presents a deterministic continuous-state semantics of PEPA which employs ordinary differential equations (ODEs) as the underlying mathematics for the performance evaluation. This is suitable for models consisting of large numbers of replicated components, as the ODE problem size is insensitive to the actual population levels of the system under study. Furthermore, the ODE is given an interpretation as the fluid limit of a properly defined CTMC model when the initial population levels go to infinity. This framework allows the use of existing results which give error bounds to assess the quality of the differential approximation. The computation of performance indices such as throughput, utilisation, and average response time are interpreted deterministically as functions of the ODE solution and are related to corresponding reward structures in the Markovian setting. The differential interpretation of PEPA provides a framework that is conceptually analogous to established approximation methods in queueing networks based on meanvalue analysis, as both approaches aim at reducing the computational cost of the analysis by providing estimates for the expected values of the performance metrics of interest. The relationship between these two techniques is examined in more detail in a comparison between PEPA and the Layered Queueing Network (LQN) model. General patterns of translation of LQN elements into corresponding PEPA components are applied to a substantial case study of a distributed computer system. This model is analysed using stochastic simulation to gauge the soundness of the translation. Furthermore, it is subjected to a series of numerical tests to compare execution runtimes and accuracy of the PEPA differential analysis against the LQN mean-value approximation method. Finally, this thesis discusses the major elements concerning the development of a software toolkit, the PEPA Eclipse Plug-in, which offers a comprehensive modelling environment for PEPA, including modules for static analysis, explicit state-space exploration, numerical solution of the steady-state equilibrium of the Markov chain, stochastic simulation, the differential analysis approach herein presented, and a graphical framework for model editing and visualisation of performance evaluation results

    Parameter dependencies for reusable performance specifications of software components

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    To avoid design-related per­for­mance problems, model-driven performance prediction methods analyse the response times, throughputs, and re­source utilizations of software architectures before and during implementation. This thesis proposes new modeling languages and according model transformations, which allow a reusable description of usage profile dependencies to the performance of software components. Predictions based on this new methods can support performance-related design decisions

    A Unifying Framework for the Approximate Solution of Closed Multiclass Queuing Networks

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    Queuing network models of modern computing systems must consider a large number of components (e.g., Web servers, DB servers, application servers, firewall, routers, networks) and hundreds of customers with very different resource requirements. The complexity of such models makes the application of exact solution techniques prohibitively expensive, motivating research on approximate methods. This paper proposes an interpolation-matching framework that allows a unified view of approximate solution techniques for closed product-form queuing networks. Depending upon the interpolating functional form and the matching populations selected, a large versatile family of new approximations can be generated. It is shown that all the known approximation strategies, including Linearizer, are instances of the interpolation-matching framework. Furthermore, a new approximation technique, based on a third-order polynomial, is obtained using the interpolation-matching framework. The new technique is shown to be more accurate than other known methods
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