Genetic Algorithms Applied to Cellular Call Admission Problem: Local Policies

It is well known that if a stochastic service system (such as a cellular network) is shared by users with diierent characteristics (such as diiering handoo rates or call holding times), the overall system performance can be improved by denial of service requests even when the excess capacity exists. Such selective denial of service based on system state is deened as call admission. A recent paper suggested the use of Genetic Algorithms to nd near-optimal call admission policies for cellular networks. In this paper, we deene local call admission policies that make admission decisions based on partial state information. We search for the best local call admission policies for one dimensional cellular networks using Genetic Algorithms and show that the performance of the best local policies is comparable to optima for small systems. We test our algorithm on larger systems and show that the local policies found outperform the maximum packing and best handoo reservation policies for the systems we have considered. We nd that the local policies suggested by the Genetic Algorithm search in these cases are double threshold policies. We then nd the best double threshold policies by exhaustive search for both one dimensional and Manhattan model cellular networks and show that they almost always outperform the best trunk reservation policies for these systems.