Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

Neural networks in B-ISDN flow control: ATM traffic prediction or network modeling?

The authors discuss a technique that offers the combination of shared bandwidth and rejection rate parameters, together with the quality of service predicted by neural networks in a novel strategy for connection admission control and call routing

Application of learning algorithms to traffic management in integrated services networks.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN027131 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Quality of service management for non-guaranteed networks

The increasing dominance of multimedia communication posed new requirements for the underlying systems. Multimedia data, formally called continuous media, has time constraints that impose real time limitations for their transmission. Certain levels of service, called Quality of Service (QoS), need to be considered when handling continuous media. The present work utilizes QoS concepts for networks that do not have inherent QoS support. The thesis aims at verifying the possibility of having QoS-controlled communication on non-guaranteed networks. A basic QoS architecture is designed where already existing QoS concepts are adapted to work with non-guaranteed networks. The architecture provides the facilities of QoS specification, mapping, admission, maintenance, monitoring and notification. In addition, a new concept for predictive QoS admission is introduced. The proposed architecture was verified using a prototype system. The results showed an increased percentage of continuous media that arrive on time to their receivers (good put) with higher network loads. The increased good put was at the expense of high network overhead

Application of an automatically designed fuzzy logic decision support system to connection admission control in ATM networks

PhDAbstract not availabl

Quality of Service optimisation framework for Next Generation Networks

Within recent years, the concept of Next Generation Networks (NGN) has become widely accepted within the telecommunication area, in parallel with the migration of telecommunication networks from traditional circuit-switched technologies such as ISDN (Integrated Services Digital Network) towards packet-switched NGN. In this context, SIP (Session Initiation Protocol), originally developed for Internet use only, has emerged as the major signalling protocol for multimedia sessions in IP (Internet Protocol) based NGN. One of the traditional limitations of IP when faced with the challenges of real-time communications is the lack of quality support at the network layer. In line with NGN specification work, international standardisation bodies have defined a sophisticated QoS (Quality of Service) architecture for NGN, controlling IP transport resources and conventional IP QoS mechanisms through centralised higher layer network elements via cross-layer signalling. Being able to centrally control QoS conditions for any media session in NGN without the imperative of a cross-layer approach would result in a feasible and less complex NGN architecture. Especially the demand for additional network elements would be decreased, resulting in the reduction of system and operational costs in both, service and transport infrastructure. This thesis proposes a novel framework for QoS optimisation for media sessions in SIP-based NGN without the need for cross-layer signalling. One key contribution of the framework is the approach to identify and logically group media sessions that encounter similar QoS conditions, which is performed by applying pattern recognition and clustering techniques. Based on this novel methodology, the framework provides functions and mechanisms for comprehensive resource-saving QoS estimation, adaptation of QoS conditions, and support of Call Admission Control. The framework can be integrated with any arbitrary SIP-IP-based real-time communication infrastructure, since it does not require access to any particular QoS control or monitoring functionalities provided within the IP transport network. The proposed framework concept has been deployed and validated in a prototypical simulation environment. Simulation results show MOS (Mean Opinion Score) improvement rates between 53 and 66 percent without any active control of transport network resources. Overall, the proposed framework comes as an effective concept for central controlled QoS optimisation in NGN without the need for cross-layer signalling. As such, by either being run stand-alone or combined with conventional QoS control mechanisms, the framework provides a comprehensive basis for both the reduction of complexity and mitigation of issues coming along with QoS provision in NGN