In communication networks today, the amount of users and traffic is constantly increasing. This results in the need for upgrading the networks to handle the demand. Free space optics is a technique which is relatively cheap with high capacity compared to most systems today. On the other hand, FSO have some disadvantages with the effects on the system by, for instance, turbulence and weather. The aim of the project is to investigate the use of network optimization for designing an optimal network in terms of capacity and cost. Routing optimization is also covered in terms of singlepath and multipath routing. To mitigate the problem with turbulence affecting the system network survivability is implemented with both proactive and reactive solutions. The method used is to implement the system in Matlab, the system should also be tested so that it works as intended. The report covers related work as well as theory behind FSO and the chosen optimization algorithms. The system uses modified Bellman-Ford optimization as well as Kruskal’s minimum spanning tree. K-link-connectivity is also implemented for the network survivability and multipath algorithm. Results of the implementation shows that the network survivability improves the robustness of the system by changing paths for traffic which is affected by broken links. Routing done by multipath will increase the throughput and also reduce the delay for the traffic.

In communication networks today, the amount of users and traffic is constantly increasing. This results in the need for upgrading the networks to handle the demand. Free space optics, FSO, is a technique which is relatively cheap with high capacity compared to most systems today. On the other hand, FSO have some disadvantages with the effects on the system by, for instance, turbulence and weather. The aim of the project is to investigate the use of network optimization for designing an optimal network in terms of capacity and cost. Routing optimization is also covered in terms of singlepath and multipath routing. To mitigate the problem with turbulence affecting the system network survivability is implemented with both proactive and reactive solutions. The method used is to implement the system in Matlab, the system should also be tested so that it works as intended. The report covers related work as well as theory behind FSO and the chosen optimization algorithms. The system uses modified Bellman-Ford optimization as well as Kruskal’s minimum spanning tree. K-link-connectivity is also implemented for the network survivability and multipath algorithm. Results of the implementation shows that the network survivability improves the robustness of the system by changing paths for traffic which is affected by broken links. Routing done by multipath will increase the throughput and also reduce the delay for the traffic.

Future development of electric power systems must pursue anumber of different goals. The power system should beeconomically efficient, it should provide reliable energysupply and should not damage the environment. At the same time,operation and development of the system is influenced by avariety of uncertain and random factors. The planner attemptsto find the best strategy from a large number of possiblealternatives. Thus, the complexity of the problems related topower systems planning is mainly caused by presence of multipleobjectives, uncertain information and large number ofvariables. This dissertation is devoted to consideration of themethods for development planning of a certain subsystem, i.e.the distribution network. The dissertation first tries to formulate the networkplanning problem in general form in terms of Bayesian DecisionTheory. However, the difficulties associated with formulationof the utility functions make it almost impossible to apply theBayesian approach directly. Moreover, when approaching theproblem applying different methods it is important to considerthe concave character of the utility function. Thisconsideration directly leads to the multi-criteria formulationof the problem, since the decision is motivated not only by theexpected value of revenues (or losses), but also by theassociated risks. The conclusion is made that the difficultiescaused by the tremendous complexity of the problem can beovercome either by introducing a number of simplifications,leading to the considerable loss in precision or applyingmethods based on modifications of Monte-Carlo or fuzzyarithmetic and Genetic Algorithms (GA), or Dynamic Programming(DP). In presence of uncertainty the planner aims at findingrobust and flexible plans to reducethe risk of considerablelosses. Several measures of risk are discussed. It is shownthat measuring risk by regret may lead to risky solutions,therefore an alternative measure - Expected Maximum Value - issuggested. The general future model, called fuzzy-probabilistictree of futures, integrates all classes of uncertain parameters(probabilistic, fuzzy and truly uncertain). The suggested network planning software incorporates threeefficient applications of GA. The first algorithm searchessimultaneously for the whole set of Pareto optimal solutions.The hybrid GA/DP approach benefits from the global optimizationproperties of GA and local search by DP resulting in originalalgorithm with improved convergence properties. Finally, theStochastic GA can cope with noisy objective functions. Finally, two real distribution network planning projectsdealing with primary distribution network in the large city andsecondary network in the rural area are studied.

Multi-channel wireless networks represent a direction that future state-of-the-art fourth generation (4G) wireless communication standards evolve towards. The IEEE 802.16 family of standards, or referred to as WiMAX, has emerged as one of the most important 4G networks to provide high speed data communication in metropolitan areas. There will be huge challenges in designing the networking protocols to allow WiMAX to provide high quality of services. How to effectively control the errors in the wireless channels and how to efficiently manage the scarce spectrum and power resources in different communication scenarios are crucial for network performance. This thesis aims to solve these challenges to improve the performance of multi-channel wireless networks, using WiMAX as a representative, through a number of techniques. First, we take advantage of the favorable properties of network coding, and design the adaptive MAC-layer and symbol-level network coding protocols. They tightly integrate with WiMAX physical and MAC layers, effectively perform error control, and efficiently utilize scarce wireless spectrum. Second, we investigate multicast services and the femto-cell architecture in WiMAX, and offer a cooperative multicast scheduling protocol as well as a cognitive WiMAX architecture with femto cells. They implement dynamic resource allocation in the networks through techniques of cooperative communication and dynamic optimization. Evaluated with rigorous analysis and extensive simulations, our proposed protocols are able to achieve substantial performance improvement over traditional protocols in the literature.

Multi-channel wireless networks represent a direction that future state-of-the-art fourth generation (4G) wireless communication standards evolve towards. The IEEE 802.16 family of standards, or referred to as WiMAX, has emerged as one of the most important 4G networks to provide high speed data communication in metropolitan areas. There will be huge challenges in designing the networking protocols to allow WiMAX to provide high quality of services. How to effectively control the errors in the wireless channels and how to efficiently manage the scarce spectrum and power resources in different communication scenarios are crucial for network performance. This thesis aims to solve these challenges to improve the performance of multi-channel wireless networks, using WiMAX as a representative, through a number of techniques. First, we take advantage of the favorable properties of network coding, and design the adaptive MAC-layer and symbol-level network coding protocols. They tightly integrate with WiMAX physical and MAC layers, effectively perform error control, and efficiently utilize scarce wireless spectrum. Second, we investigate multicast services and the femto-cell architecture in WiMAX, and offer a cooperative multicast scheduling protocol as well as a cognitive WiMAX architecture with femto cells. They implement dynamic resource allocation in the networks through techniques of cooperative communication and dynamic optimization. Evaluated with rigorous analysis and extensive simulations, our proposed protocols are able to achieve substantial performance improvement over traditional protocols in the literature.

<p>Future development of electric power systems must pursue anumber of different goals. The power system should beeconomically efficient, it should provide reliable energysupply and should not damage the environment. At the same time,operation and development of the system is influenced by avariety of uncertain and random factors. The planner attemptsto find the best strategy from a large number of possiblealternatives. Thus, the complexity of the problems related topower systems planning is mainly caused by presence of multipleobjectives, uncertain information and large number ofvariables. This dissertation is devoted to consideration of themethods for development planning of a certain subsystem, i.e.the distribution network.</p><p>The dissertation first tries to formulate the networkplanning problem in general form in terms of Bayesian DecisionTheory. However, the difficulties associated with formulationof the utility functions make it almost impossible to apply theBayesian approach directly. Moreover, when approaching theproblem applying different methods it is important to considerthe concave character of the utility function. Thisconsideration directly leads to the multi-criteria formulationof the problem, since the decision is motivated not only by theexpected value of revenues (or losses), but also by theassociated risks. The conclusion is made that the difficultiescaused by the tremendous complexity of the problem can beovercome either by introducing a number of simplifications,leading to the considerable loss in precision or applyingmethods based on modifications of Monte-Carlo or fuzzyarithmetic and Genetic Algorithms (GA), or Dynamic Programming(DP).</p><p>In presence of uncertainty the planner aims at findingrobust and flexible plans to reducethe risk of considerablelosses. Several measures of risk are discussed. It is shownthat measuring risk by regret may lead to risky solutions,therefore an alternative measure - Expected Maximum Value - issuggested. The general future model, called fuzzy-probabilistictree of futures, integrates all classes of uncertain parameters(probabilistic, fuzzy and truly uncertain).</p><p>The suggested network planning software incorporates threeefficient applications of GA. The first algorithm searchessimultaneously for the whole set of Pareto optimal solutions.The hybrid GA/DP approach benefits from the global optimizationproperties of GA and local search by DP resulting in originalalgorithm with improved convergence properties. Finally, theStochastic GA can cope with noisy objective functions.</p><p>Finally, two real distribution network planning projectsdealing with primary distribution network in the large city andsecondary network in the rural area are studied.</p>

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

This article studies the linear multicommodity network flow problem. This kind of problem arises in a wide variety of contexts. A numerical implementation of the primal-dual interior-point method is designed to solve the problem. In the interior-point method, at each iteration, the corresponding linear system, expressed as a normal equations system, is solved by using the AINV algorithm combined with a preconditioned conjugate gradient algorithm or by the AINV algorithm for the whole normal equations. Numerical experiments are conducted for networks of different dimensions and numbers of products for the distribution problem. The computational results show the effectiveness of the interior-point method for this class of network problems.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate