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794 Random Variables and Processes A Poisson process has many important properties that make it easier to analyze systems with Poisson traffic than other forms of traffic. See [BG92] for a good summary. H.2.2 Gaussian Random Process In many cases, we model noise as a wide-sense stationary Gaussian random process X(t). It is also common to assume that at any two instants of time t 1 = t 2 the random variables X(t 1 ) and X(t 2 ) are independent Gaussian variables with mean . For such a process, we can use (H.1) and write E[X 2 (t)X 2 (t + )] = (E[X 2 (t)]) 2 + 2(E[X(t)]E[X(t + )]) 2 , that is, 2 2 E[X 2 (t)X 2 (t + )] = R X (0) + 2R X ( ). Further Reading There are several good books on probability and random processes. See, for example, [Pap91, Gal99]. References [BG92] D. Bertsekas and R. G. Gallager. Data Networks. Prentice Hall, Englewood Cliffs, NJ, 1992. [Gal99] R. G. Gallager. Discrete Stochastic Processes. Kluwer, Boston, 1999. [Pap91] A. Papoulis. Probability, Random Variables, and Stochastic Processes, 3rd edition. McGraw-Hill, New York, 1991.