The cognitive radio (CR) concept is expected to be adopted along with many technologies to meet the requirements of the next generation of wireless and mobile systems, the 5G. Consequently, it is important to determine the performance of the CR systems with respect to these requirements. In this thesis, after briefly describing the 5G requirements, we present three main directions in which we aim to enhance the CR performance. The first direction is the reliability. We study the achievable rate of a multiple-input multiple-output (MIMO) relay-assisted CR under two scenarios; an unmanned aerial vehicle (UAV) one-way relaying (OWR) and a fixed two-way relaying (TWR). We propose special linear precoding schemes that enable the secondary user (SU) to take advantage of the primary-free channel eigenmodes. We study the SU rate sensitivity to the relay power, the relay gain, the UAV altitude, the number of antennas and the line of sight availability. The second direction is the scalability. We first study a multiple access channel (MAC) with multiple SUs scenario. We propose a particular linear precoding and SUs selection scheme maximizing their sum-rate. We show that the proposed scheme provides a significant sum-rate improvement as the number of SUs increases. Secondly, we expand our scalability study to cognitive cellular networks. We propose a low-complexity algorithm for base station activation/deactivation and dynamic spectrum management maximizing the profits of primary and secondary networks subject to green constraints. We show that our proposed algorithms achieve performance close to those obtained with the exhaustive search method. The third direction is the energy efficiency (EE). We present a novel power allocation scheme based on maximizing the EE of both single-input and single-output (SISO) and MIMO systems. We solve a non-convex problem and derive explicit expressions of the corresponding optimal power. When the instantaneous channel is not available, we present a simple sub-optimal power that achieves a near-optimal EE. The simulations show that the sub-optimal solution is very close to the optimal one. In the MIMO case, we show that adopting more antennas is more energy efficient.

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section.

Demand for high data rates is increasing rapidly for the future wireless generations, due to the requirement ofubiquitous coverage for wireless broadband services. More base stations are needed to deliver these services, in order tocope with the increased capacity demand and inherent unreliable nature of wireless medium. However, this would directly correspond to high infrastructure costand energy consumption in cellular networks. Nowadays, high power consumption in the network is becoming a matter of concern for the operators,both from environmental and economic point of view. Cooperative communications, which is regarded as a virtual multi-input-multi-output (MIMO) channel, can be very efficient in combating fading multi-path channels and improve coverage with low complexity and cost. With its distributed structure, cooperativecommunications can also contribute to the energy efficiency of wireless systems and green radio communications of the future. Using networkcoding at the top of cooperative communication, utilizes the network resources more efficiently. Here we look at the case of large scale use of low cost relays as a way of making the links reliable, that directly corresponds to reductionin transmission power at the nodes. A lot of research work has focused on highlighting the gains achieved by using network codingin cooperative transmissions. However, there are certain areas that are not fully explored yet. For instance, the kind of detectionscheme used at the receiver and its impact on the link performance has not been addressed.The thesis looks at the performancecomparison of different detection schemes and also proposes how to group users at the relay to ensure mutual benefit for the cooperating users.Using constellation selection at the nodes, the augmented space formed at the receiver is exploited for making the links more reliable. Thenetwork and the channel coding schemes are represented as a single product code, that allows us to exploit the redundancy present in theseschemes efficiently and powerful coding schemes can also be designed to improve the link performance. Heterogeneous network deployments and adaptive power management has been used in order to reduce the overall energy consumption in acellular network. However, the distributed structure of nodes deployed in the network, is not exploited in this regard. Here we have highlightedthe significance of cooperative relaying schemes in reducing the overall energy consumption in a cellular network.  The role of differenttransmission and adaptive resource allocation strategies in downlink scenarios have been investigated in this regard.It has been observed that the adaptive relaying schemes can significantly reduce the total energy consumption as compared to the conventionalrelaying schemes. Moreover, network coding in these adaptive relaying schemes, helps in minimizing the energy consumption further.The balance between the number of base stations and the relays that minimizes the energy consumption, for each relaying scheme is also investigated. / QC 20120124

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The reliability and efficiency of ANN based distance relays is improving with the developing digital technologies. There are, however, some inherent deficiencies that still exist in the way these relays are designed. This research addresses some of these issues and proposes an improved protective relaying scheme.<p> The traditional ANN distance relay designs use parameter estimation algorithms to determine the phasors of currents and voltages. These phasors are used as inputs to determine the distance of a fault from relay location. The relays are trained and tested on this criterion; however, no specific relay characteristic has been defined. There is a need for development of a new methodology that will enable designing of an ANN that works as a generic distance relay with clearly defined operating boundary.<p> This research work presents a modified distance relaying algorithm that has been combined with a neural network approach to eliminate the use of phasors. The neural network is trained to recognize faults on basis of a specific relay characteristic. The algorithm is flexible and has been extended for the design of other relays. The neural network has been trained using pure sinusoidal values and has been tested on a 17-bus power system simulated in PSCAD. The training and testing of the neural network on different systems ensures that the relay is generic in nature. The proposed relay can be used on any transmission line without re-training the neural network.<p> The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries.

There has been significant development in the area of neural network based power system protection in the previous decade. Neural network technology has been applied for various protective relaying functions including distance protection. The reliability and efficiency of ANN based distance relays is improving with the developing digital technologies. There are, however, some inherent deficiencies that still exist in the way these relays are designed. This research addresses some of these issues and proposes an improved protective relaying scheme.<p> The traditional ANN distance relay designs use parameter estimation algorithms to determine the phasors of currents and voltages. These phasors are used as inputs to determine the distance of a fault from relay location. The relays are trained and tested on this criterion; however, no specific relay characteristic has been defined. There is a need for development of a new methodology that will enable designing of an ANN that works as a generic distance relay with clearly defined operating boundary.<p> This research work presents a modified distance relaying algorithm that has been combined with a neural network approach to eliminate the use of phasors. The neural network is trained to recognize faults on basis of a specific relay characteristic. The algorithm is flexible and has been extended for the design of other relays. The neural network has been trained using pure sinusoidal values and has been tested on a 17-bus power system simulated in PSCAD. The training and testing of the neural network on different systems ensures that the relay is generic in nature. The proposed relay can be used on any transmission line without re-training the neural network.<p> The design has been tested for different fault conditions including different fault resistances and fault inception angles. The test results show that the relay is able to detect faults in lesser time as compared to conventional relay algorithms while maintaining the integrity of relay boundaries.

Due to faults and switching, transmission lines encounter power oscillations referred as power swings. Although in most cases they do not lead to an eventual instability, severe changes in power flows on the lines may cause the operation of impedance relays incorrectly, leading to cascaded tripping of other lines. Out-of-Step tripping function is employed in modern distance relays to distinguish such an unstable swing but setting the parameters and deciding lines to be tripped require detailed dynamic power system modelling and analysis. The proposed method aims to determine possible out-of-step (OOS) locations on a power system without performing detailed dynamic simulations. Method presented here, is based on grouping of the buses by statistical clustering analysis of the network impedance matrix. Inter-cluster lines are shown to be more vulnerable to give rise to OOS as proven with dynamic simulations on IEEE 39 bus test system.

WiMAX is one of the rising communications technology which enables last mile broadband mobile wireless Internet connectivity. IEEE Std 802.16-2009 is the last accepted standard which targets mobile and fixed wireless broadband access. The standard defines two types of stations which are base and mobile stations. A base station has a wired connection to backhaul network and gives broadband wireless service to mobile stations. IEEE 802.16j standard which is an amendment to IEEE 802.16, introduces Multihop Relaying for increasing coverage and throughput. Deployment of relay stations, where the backbone network does not exist, is a cost effective solution. Two modes of operations are defined for relay station: transparent mode and non-transparent mode. Relays in transparent mode, are deployed for improving signal quality, so that mobile stations can use relay link for increasing throughput. In non-transparent mode, relays can send management packets, so that mobile stations, which are not in the direct reach of a base station, can connect to network through relay stations. In domain specific networks main data traffic is caused by the communication between subscribers in same region. In this thesis shortcut routing scheme is proposed as sending packets to destination directly through relay station for data traffic between two subscribers with a common relay. With shortcut routing, network throughput is increased by preventing links at higher layer in topology from becoming bottleneck. Moreover, by traversing fewer hops, latency decreases. We also propose traffic aware path selection method, where a path will more.

Modern wireless communication systems strive to enable communications at high data rates, over wide geographical areas, and to multiple users. Unfortunately, this can be a daunting task in practice, as natural laws governing the wireless medium may hinder point-to-point transmissions. Communications over large distances (path loss), and physical obstructions in line-of-sight signals (shadowing) are prime examples of such impediments. One promising solution is to deploy intermediary terminals to help reestablish such broken point-to-point communication links. Such terminals are called relay nodes, and the corresponding systems are referred to as being relay-aided. As in the case of point-to-point communication, design of efficient transmission and reception techniques in relay-aided systems depends on the availability of propagational channel state information. In practice, such information is only accurate to a certain degree which is governed by overhead constraints, feedback delay, and channel fluctuations due to mobility. Understanding the impacts of such partial channel state information, and devising transmission and reception methods based on such understandings, is the main topic of this dissertation. The transmission protocol classifies relays as either one-way, where the relay receives signals from one terminal, or two-way, where the relay receives signals from more than one terminal. Designs and solutions for both one- and two-way relaying systems are presented in this dissertation. Emphasis is placed on two-way relaying systems given their superior efficiency in utilizing channel resources. For one-way relaying this dissertation presents power loading strategies for multiuser-multicast systems derived based on the availability of full or partial channel state information at the terminals. In the case of two-way relaying, both single and multi-user systems are analyzed. For single-user two-way relaying, this dissertation presents optimal methods of acquiring partial channel state information via pilot-aided channel estimation methods. This includes an analysis of the effects of channel estimation upon the system sum-rate. Also, the design of channel equalizers exhibiting robustness to partial channel state information is proposed. For multi-user two-way relaying, this dissertation presents several precoding strategies at the relay terminal(s) to combat the effects co-channel interference in light of the existence of self-interference inherent to two-way relaying operations. / text

Cognitive radio technology is a promising technology to solve the wireless spectrum scarcity problem by intelligently allowing secondary, or unlicensed, users access to the primary, licensed, users' frequency bands. Cognitive technology involves two main tasks: 1) sensing the wireless medium to assess the presence of the primary users and 2) designing secondary spectrum access techniques that maximize the secondary users' benefits while maintaining the primary users' privileged status. On the spectrum sensing side, we make two contributions. First, we maximize a utility function representing the secondary throughput while constraining the collision probability with the primary below a certain value. We optimize therein the channel sensing time, the sensing decision threshold, the channel probing time, together with the channel sensing order for wideband primary channels. Second, we design a cooperative spectrum sensing technique termed sensing with equal gain combining whereby cognitive radios simultaneously transmit their sensing results to the fusion center over multipath fading reporting channels. The proposed scheme is shown to outperform orthogonal reporting systems in terms of achievable secondary throughput and to be robust against phase and synchronization errors. On the spectrum access side, we make four contributions. First, we design a secondary scheduling scheme with the goal of minimizing the secondary queueing delay under constraints on the average secondary transmit power and the maximum tolerable primary outage probability. Second, we design another secondary scheduling scheme based on the spectrum sensing results and the primary automatic repeat request feedback. The optimal medium access probabilities are obtained via maximizing the secondary throughput subject to constraints that guarantee quality of service parameters for the primary. Third, we propose a three-message superposition coding scheme to maximize the secondary throughput without degrading the primary rate. Cognitive relaying is employed as an incentive for the primary network. The scheme is shown to outperform a number of reference schemes such as best relay selection. Finally, we consider a network of multiple primary and secondary users. We propose a three-stage distributed matching algorithm to pair the network users. The algorithm is shown to perform close to an optimal central controller, albeit at a reduced computational complexity.

This dissertation introduces advanced artificial intelligence based algorithm for detecting and classifying faults on the power system transmission line. The proposed algorithm is aimed at substituting classical relays susceptible to possible performance deterioration during variable power system operating and fault conditions. The new concept relies on a principle of pattern recognition and detects the existence of the fault, identifies fault type, and estimates the transmission line faulted section. The approach utilizes self-organized, Adaptive Resonance Theory (ART) neural network, combined with fuzzy decision rule for interpretation of neural network outputs. Neural network learns the mapping between inputs and desired outputs through processing a set of example cases. Training of the neural network is based on the combined use of unsupervised and supervised learning methods. During training, a set of input events is transformed into a set of prototypes of typical input events. During application, real events are classified based on the interpretation of their matching to the prototypes through fuzzy decision rule. This study introduces several enhancements to the original version of the ART algorithm: suitable preprocessing of neural network inputs, improvement in the concept of supervised learning, fuzzyfication of neural network outputs, and utilization of on-line learning. A selected model of an actual power network is used to simulate extensive sets of scenarios covering a variety of power system operating conditions as well as fault and disturbance events. Simulation results show improved recognition capabilities compared to a previous version of ART neural network algorithm, Multilayer Perceptron (MLP) neural network algorithm, and impedance based distance relay. Simulation results also show exceptional robustness of the novel ART algorithm for all operating conditions and events studied, as well as superior classification capabilities compared to the other solutions. Consequently, it is demonstrated that the proposed ART solution may be used for accurate, high-speed distinction among faulted and unfaulted events, and estimation of fault type and fault section.