An important, yet difficult, problem in the design of a packet radio network is the determination of a conflict-free broadcast schedule at a minimum cycle length. We first formulate the problem via a within-two-hop connectivity matrix and then, by assuming a known cycle length, determine a conflict-free scheduling pattern using a centralized approach that exploits the structure of the problem via a modified genetic algorithm. This algorithm, called genetic-fix, generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and therefore, can reduce the search space substantially. We also propose a method to find a reasonable cycle length and shorten it gradually to obtain a near-optimal one. Simulations on three benchmark problems showed that our approach could achieve 100% convergence to solutions with optimal cycle length within reasonable time.

The future telecommunication networks are expected to support a wide variety of multimedia services with very different bit-rate and performance requirements. Under these circumstances, it has been recognized that the conventional simple source modeling, namely, the Poisson arrival process, is no longer adequate to describe various kinds of traffic sources accurately. Other source modelings, which give more accurate results but greatly complicate the performance analyses, have been proposed. However, all these models share a common property, that is, they can be decomposed into a superposition of independent identical interrupted Poisson processes (IPP). In order to obtain a balance between the accuracy and the complexity of analysis, the authors propose to approximate an IPP, a basic component of various models, by a Poisson distribution and use the Chen-Stein (1975, 1972) method to find an upper bound of this approximation error.>

An autoregressive (AR) model which is excited by a non-zero-mean, independent and identically distributed stationary random process is investigated. As in the zero-mean case, a cumulant-based higher-order Yule-Walker equation is derived. By expanding the cumulants in terms of their moments, a higher-order moment formula is obtained. This formula not only relates the higher-order moment with the lower-order moment, but also makes it possible to estimate the AR parameters and the output mean simultaneously. The formula is computationally more efficient than the cumulant formula.>