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Download e-book for iPad: Computational intelligence in telecommunications networks by Witold Pedrycz, Athanasios Vasilakos

By Witold Pedrycz, Athanasios Vasilakos

ISBN-10: 084931075X

ISBN-13: 9780849310751

Telecommunications has advanced and grown at an explosive price in recent times and should definitely proceed to take action. As its features, purposes, and expertise develop, it turns into more and more advanced and hard, if no longer most unlikely, to satisfy the calls for of a world community utilizing traditional computing applied sciences. Computational intelligence (CI) is the expertise of the future-and the long run is now. Computational Intelligence in Telecommunications Networks bargains an in-depth examine the speedy development of CI know-how and exhibits its value in fixing the the most important difficulties of destiny telecommunications networks. It covers a extensive variety of issues, from name Admission regulate, congestion regulate, and QoS-routing for ATM networks, to community layout and administration, optical, cellular, and energetic networks, and clever cellular Agents.Today's telecommunications pros want a operating wisdom of CI to take advantage of its capability to beat rising demanding situations. The CI group needs to develop into familiar with these demanding situations to use the large possibilities the telecommunications box deals. this article meets either these wishes, essentially, concisely, and with a intensity sure to motivate additional theoretical and useful advances.

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4. Jitter. In this case, the NN predicts the variation of the buffer delay. 5. Bandwidth. In this case, the NN predicts the amount of bandwidth needed to achieve a specific QoS level for the given input stream. 6. p-th percentile delay. In this case, the NN predicts the value D such that the probability that a cell or packet experiences a delay less than or equal to D is p%. The percentile is typically chosen to be around 90%. In this approach, all the calls need to have the same percentile requirement.

When FIFO queuing is used, there is only a single loss rate or distribution of delay associated with that queue. The NN predicts the QoS of the aggregate stream of superposed traffic sources. When many sessions that have different QoS requirements are multiplexed together, the switch ensures the most stringent of all the delay and/or loss requirements. ) Thus this model can support multiple traffic classes, but they will all receive the same QoS. , earliest deadline first) is used to prioritize among traffic classes, then multiple loss rates (one for each class) could be computed for a single buffer.

Neural networks do not require an accurate mathematical model of either the traffic or the system. No assumptions need to be made since the neural network is trained on observed data. Not assuming a specific traffic behavior a priori is a preferable approach because multimedia traffic is not well understood and continuously changing. NNs are also not affected by mistakes in declared traffic descriptors. These features allow a NN to yield more accurate QoS estimation, which leads to greater efficiency and robustness.

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Computational intelligence in telecommunications networks by Witold Pedrycz, Athanasios Vasilakos


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