Generation of ATM video traffic using neural networks

A new model to generate Asynchronous Transfer Mode (ATM) video traffic is presented. The model, implemented on neural networks, is capable of accurately adjusting the autocorrelation and probability distribution functions of a given video traffic. This adjustment is performed by capturing the projected conditioned histogram of the real traffic, so that the neural model will be able to yield a simulated traffic as a function of an input white noise. Using neural networks we benefit from their inherent capacities for working in real time, because of their parallelism, and interpolating unknown functions. Results are presented for a real MPEG video source.