Modern cellular mobile communications systems are characterized by a high degree of capacity. Consequently, they have to serve the maximum possible number of calls while the number of channels per cell is limited. The objective of channel allocation is to assign a required number of channels to each cell such that both efficient frequency spectrum utilization is provided and interference effects are minimized. Channel assignment is therefore an important operation of resource management and its efficient implementation increases the fidelity, capacity, and quality of service of cellular systems. Most channel allocation strategies are based on deterministic methods, however, which result in implementation complexity that is prohibitive for the traffic demand envisaged for the next generation of mobile systems. An efficient heuristic technique capable of handling channel allocation problems is introduced here as an alternative. The method is called a combinatorial evolution strategy (CES) and belongs to the general heuristic optimization techniques known as evolutionary algorithms (EA's). Three alternative allocation schemes operating deterministically, namely the dynamic channel assignment (DCA), the hybrid channel assignment (HCA), and the borrowing channel assignment (BCA), are formulated as combinatorial optimization problems for which CES is applicable. Simulations for representative cellular models show the ability of this heuristic to yield sufficient solutions. These results will encourage the use of this method for the development of a heuristic channel allocation controller capable of coping with the traffic and spectrum management demands for the proper operation of the next generation of cellular systems.

The demand for more efficient and fast channel allocation techniques in cellular systems increases day by day. Borrowing channel assignment (BCA) was introduced in the literature as a compromise between the classic fixed and dynamic channel allocation schemes. This paper examines the behavior of three heuristic BCA techniques based alternatively on a Hopfield neural network, an efficient evolutionary algorithm named combinatorial evolution strategy (CES) and a third heuristic which combines the basic advantages of the two above computational intelligence methods. By considering some specific assumptions that follows an ideal cellular mobile model, BCA is formulated as a combinatorial optimization problem. The above heuristics have been extensively applied to solve efficiently such problems in the past. Simulation results, derived for uniform and nonuniform traffic load conditions, are used to compare these BCA schemes each other as also with other well-established allocation techniques.

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