6,351 research outputs found

    Statistical multiplexing of video sources for packet switching networks

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    Communication networks are fast evolving towards truly integrated networks handling all types of traffic. They employ integrated switching technologies for voice. video and data. Statistical or asynchronous time division multiplexing of full motion video sources is an initial step towards packetized video networks. The main goal is to utilize the common communication channel efficiently, without loosing quality at the receiver. This work discusses the concept of using statistical multiplexing for packet video communications. The topology of a single internal packet network to support ISDN services has been adopted. Simulations have been carried out to demonstrate the statistical smoothing effect of packetized video in the networks having high speed links. Results indicate that the channel rate per source decreased in an exponential manner as the number of sources increased. An expression for the average usage time t of the channel has been derived in terms of channel rate per source and the number of sources multiplexed. Also the average usage time of the channel is higher for buffered data than that of the multiplexed data. The high speed communication links in the internal network are lightly loaded, which indicates that these links can accommodate more data

    Rate Control for VBR Video Coders in Broadband Networks

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    Traffic control mechanisms with cell rate simulation for ATM networks.

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    Some aspects of traffic control and performance evaluation of ATM networks

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    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

    Comb-based WDM transmission at 10 Tbit/s using a DC-driven quantum-dash mode-locked laser diode

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    Chip-scale frequency comb generators have the potential to become key building blocks of compact wavelength-division multiplexing (WDM) transceivers in future metropolitan or campus-area networks. Among the various comb generator concepts, quantum-dash (QD) mode-locked laser diodes (MLLD) stand out as a particularly promising option, combining small footprint with simple operation by a DC current and offering flat broadband comb spectra. However, the data transmission performance achieved with QD-MLLD was so far limited by strong phase noise of the individual comb tones, restricting experiments to rather simple modulation formats such as quadrature phase shift keying (QPSK) or requiring hard-ware-based compensation schemes. Here we demonstrate that these limitations can be over-come by digital symbol-wise phase tracking algorithms, avoiding any hardware-based phase-noise compensation. We demonstrate 16QAM dual-polarization WDM transmission on 38 channels at an aggregate net data rate of 10.68 Tbit/s over 75 km of standard single-mode fiber. To the best of our knowledge, this corresponds to the highest data rate achieved through a DC-driven chip-scale comb generator without any hardware-based phase-noise reduction schemes

    Quality of service over ATM networks

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