Observations on using genetic-algorithms for channel allocation in mobile computing

Abstract

This paper highlights the potential of using genetic algorithms to solve cellular resource allocation problems. The objective in this work is to gauge how well a GA-based channel borrower performs when compared to a greedy borrowing heuristic. This is needed to establish how suited GA-like (stochastic search) algorithms are for the solution of optimization problems in mobile computing environments. This involves the creation of a simple mobile networking resource environment and design of a GA-based channel borrower that works within this environment. A simulation environment is also built to compare the performance of the GA-based channel-borrowing method with the heuristic. To enhance the performance of the GA, extra attention is paid to developing an improved mutation operator. The performance of the new operator is evaluated against the heuristic borrowing scheme. For a real-time implementation, the GA needs to have the properties of a micro GA strategy. This involves making improvements to the crossover operator and evaluation procedure so the GA can converge to a "good" solution rapidly.