Ibrahim W. Habib

Adaptive allocation of resources and call admission control for wireless ATM using genetic algorithms

In wireless ATM-based networks, admission control is required to reserve resources in advance for calls requiring guaranteed services. In the case of a multimedia call, each of its substreams (i.e., video, audio, and data) has its own distinct quality of service (QoS) requirements (e.g., cell loss rate, delay, jitter, etc.). The network attempts to deliver the required QoS by allocating an appropriate amount of resources (e.g., bandwidth, buffers). The negotiated QoS requirements constitute a certain QoS level that remains fixed during the call (static allocation approach). Accordingly, the corresponding allocated resources also remain unchanged. We present and analyze an adaptive allocation of resources algorithm based on genetic algorithms. In contrast to the static approach, each substream declares a preset range of acceptable QoS levels (e.g., high, medium, low) instead of just a single one. As the availability of resources in the wireless network varies, the algorithm selects the best possible QoS level that each substream can obtain. In case of congestion, the algorithm attempts to free up some resources by degrading the QoS levels of the existing calls to lesser ones. This is done, however, under the constraint of achieving maximum utilization of the resources while simultaneously distributing them fairly among the calls. The degradation is limited to a minimum value predefined in a user-defined profile (UDP). Genetic algorithms have been used to solve the optimization problem. From the user perspective, the perception of the QoS degradation is very graceful and happens only during overload periods. The network services, on the other hand, are greatly enhanced due to the fact that the call blocking probability is significantly decreased. Simulation results demonstrate that the proposed algorithm performs well in terms of increasing the number of admitted calls while utilizing the available bandwidth fairly and effectively.

Controlling flow and avoiding congestion in broadband networks

The basic design principles of flow control in asynchronous transfer mode (ATM) networks are examined. Network-level, call-level and cell-level flow control are covered. Dynamic bandwidth management and scheduling policies are discussed.<<ETX>>

Adaptive modulation and coding techniques for OFDMA systems

The demand for high-speed services and multimedia applications anywhere and anytime has led to the rise of wireless communications. In particular, WiMAX technology is nowadays considered one of the most prominent solutions capable to provide a Broadband Wireless Access (BWA) in metropolitan areas with a simpler installation and lower cost than traditional wired alternatives. This paper deals with the proposal of efficient adaptive modulation and coding techniques to be used in WiMAX based wireless networks, that allow to improve network performance in the case of Non Line-of-Sight communications, which are typical in urban environments. Through these techniques it is possible to switch the modulation order and coding rate in order to better match the channel conditions, and, hence, obtaining better performance both in terms of error probability and data throughput.

TCP Fairness Issues in IEEE 802.11 Networks: Problem Analysis and Solutions Based on Rate Control

In this paper, we study the problem of maintaining fairness for TCP connections in wireless local area networks (WLANs) based upon the IEEE 802.11 standard. Current implementations of 802.11 use the so-called distributed coordination function (DCF), which provides similar medium access priority to all stations. Although this mode of operation ensures fair access to the medium at the MAC level, it does not provide any provisions for ensuring fairness among the TCP connections. TCP unfairness may result in significant degradation of performance leading to users perceiving unsatisfactory quality of service. We propose and analyze two solutions that are capable of enabling TCP fairness with minimal additional complexity. The proposed solutions are based on utilizing a rate-control mechanism in two modes: static or adaptive. They do not require modifying existing standards at the MAC or network layers. Hence, they are fully compatible with existing devices. Our performance analysis results prove the efficaciousness of our proposed solutions in achieving TCP fairness compared to existing approaches. We have, also, implemented the proposed solutions in an ad-hoc experimental test-bed, and performed measurements to demonstrate the validity of our approach and results

Polynomial distance measurement for ECG based biometric authentication

Existing electrocardiography (ECG) based biometric systems are constantly being challenged by higher misclassification error, longer acquisition time, larger template size, slower processing time and pertinence of abnormal beats within the biometric template. These challenges are the prime hindrance for ECG based biometric being commercialized as a pervasive authentication mechanism. At least,ECGbased biometric can provide a secured mechanism for cardiac patients being monitored over telephony network. In this paper, we present a polynomial distance measurement (PDM) method for ECG based biometric authentication for the very first time, according to the literature and to the best of our knowledge. The proposed PDM method is up to 12 times faster than existing algorithms, requires up to 6.5 times less template storage, needs only 2.49 (average) acquisition time with the highest accuracy rate (up to 100 per cent) when experimented on a population size of 15. Moreover, this proposed ECG based biometric system was deployed on a mobile phone based telemonitoring scenario with multilayer authentication mechanism upholding its applicability.

Intelligent traffic control for ATM broadband networks

Performance results prove that a neural networks approach achieves better results, simpler and faster, than algorithmic approaches. The focus of this paper is to shed light on how neural networks (NNs) can be used to solve many of the serious problems encountered in the development of a coherent traffic control strategy in ATM networks. The main philosophy that favors neural networks over conventional programming approaches is their learning and adaptive capabilities, which can be utilized to construct adaptive (and computationally intelligent) algorithms for allocation of resources (e.g., bandwidth, buffers), thus providing highly effective tools for congestion control. >

A neural network controller for congestion control in ATM multiplexers

Abstract This paper presents and adaptive approach to the problem of congestion control arising at the User-to Network Interface (UNI) of an ATM multiplexer. We view the ATM multiplexer as a non-linear stochastic system whose dynamics are ill-defined. Real-time measurements of the arrival rate process and the queueing process, are used to identify, and minimize congestion episodes. The performance of the system is evaluated using a performance-index function which is a quantative measure of “how well” the system is performing. A three-layers backpropagation neural network controller generates a signal that attempts to minimize congestion without degrading the quality of the traffic. During periods of buffer over-load the control signal, adaptively, modulates the arrival process such that its peak-rate is throttled-down. As soon as congestion is terminated, the control signal is adjusted such that the coding rates are restored back to their original values. Adaptability is achieved by continuously adjusting the weights of the neural network controller such that the performance of the system, measured by its performance index function, is maximized over a certain optimization period. The performance index function is defined in terms of two main objectives: (1) to minimize the cell loss rate (CLR), i.e., minimize congestion episodes, and (2) to maintain the quality of the video/audio traffic by maintaining its original source coding rate. The neural network learning process can be viewed as a specialized form of reinforcement learning in the sense that the control signal is reinforced if it tends to maximize the performance index function. Performance evaluation results prove that this approach is effective in controlling congestion while maintaining the quality of the traffic.

Deployment of the GMPLS control plane for grid applications in experimental high-performance networks

In this article we present a review of the latest activities in recent experimental high-performance optical networks such as ultrascience network (USN), dynamic resource allocation via GMPLS optical network (DRAGON), and circuit-switched high-speed end-to-end transport architecture (CHEETAH). We compare the control and management approaches adopted in each of these networks and analyze their capabilities vis-a-vis the functional requirements of grid computing applications. Grid computing is increasingly on the rise to meet the massive processing and storage demands of a new class of e-science physics applications that may generate and require the processing of data sets reaching terabytes per day. The requirements of these applications challenge the limitations of the networking technologies that are in place today. In particular, the area of network management and control is undergoing significant developments in order to meet the demands of these applications. It is the purpose of this article to share our experiences in the deployment of the GMPLS control plane in these experimental optical networks. It is our belief that these and similar efforts will result in significant progress toward enabling connection-oriented high-performance networking. This new paradigm will encompass grid computing applications as well as commercial, health, and entertainment services, thus making it useful to the public at large.

A neurocomputing controller for bandwidth allocation in ATM networks

We propose a new neurocomputing call admission control (CAC) algorithm for asynchronous transfer mode (ATM) networks. The proposed algorithm employs neural networks (NNs) to calculate the bandwidth required to support multimedia traffic with multiple quality-of-service (QoS) requirements. The NN controller calculates the bandwidth required percall using on-line measurements of the traffic via its count process, instead of relying on simple parameters such as the peak, average bit rate and burst length. Furthermore, to enhance the statistical multiplexing gain, the controller calculates the gain obtained from multiplexing multiple streams of traffic supported on separate virtual paths (i.e., class multiplexing). In order to simplify the design and obtain a small reaction time, the controller is realized using a hierarchical structure of a bank of small size, parallel NN units. Each unit is a feed-forward back-propagation NN that has been trained to, learn the complex nonlinear function relating different traffic patterns and QoS, with the corresponding received capacity. The reported results prove that the neurocomputing approach is effective in achieving more accurate results than other conventional methods that are based upon mathematical or simulation analysis. This is primarily due to the unique learning and adaptive capabilities of NNs that enable them to extract and memorize rules from previous experience. Evidently such unique capabilities poise NNs to solve many of the problems encountered in the development of a coherent ATM traffic management strategy.

Scheduling for differentiated traffic types in HSDPA cellular systems

This paper proposes a novel packet scheduler for the high speed downlink packet access (HSDPA) air interface. In designing the scheduler we take in account two different user types and two distinct traffic classes. Our aim is to differentiate the quality of service and at the same time to achieve a high throughput and a fair allocation of resources among users. The performance of the proposed scheme is compared through simulations with other scheduling techniques (e.g., maximum SIR). This work is the outcome of a joint activity between the University of Siena and CUNY.