An efficient evolutionary algorithm for channel resource management in cellular mobile systems

Abstract

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.

A Hybrid Neural-Genetic Algorithm for the Frequency Assignment Problem in Satellite Communications

“…(2) On-line algorithms for dynamically assigning frequencies to users in a established network (mainly wireless communication networks) [5,6].…”

A portable and scalable algorithm for a class of constrained combinatorial optimization problems

“…The most classical example of this characterization is the frequency assignment problem in a mobile communication network [3,5,7,15]. In this problem, every element of the matrix C; c ij , represents the minimum distance between frequencies in the communication network needed to avoid interferences, whereas f(X ) is the number of frequencies assigned, see [3,4] for details.…”

“…Problems of this kind arise profusely in computational engineering and computer science ÿelds. Scheduling problems [1,2], assignment problems [3][4][5][6]27] or problems of systems design [7][8][9], are some examples. In this paper, we refer to this kind of problems as constrained combinatorial optimization problems (CCOPs).…”

A Useful Metaheuristic for Dynamic Channel Assignment in Mobile Cellular Systems

“…To overcome the difficulties associated with the deterministic methods other heuristic methods such as Simulated Annealing [5], Tabu Search [6], Neural Networks [7,8] and Genetic Algorithms [9,11,12] were used for the solution of FCA problem. Later, Feedforward Neural Networks [19], Hopfield Neural Networks [20,21], Genetic Algorithms [10,13], Combinatorial Evolution Strategy [22], and Particle Swarm Optimization [17] have been used for the solution of DCA problem. However, the ever increasing number of mobile cellular users and the increasing demand for bandwidth call for more and more efficient Dynamic Channel Assignment strategies.…”

“…Metaheuristic Search techniques prove effective in the solution of DCA problem. A number of approaches [5][6][7][8][9][10][11][12][13]17,19,20,21,22] have been proposed for the solution of Channel Assignment Problem. The initial efforts for the solution of FCA were based on deterministic methods but as the problem is NP-hard these methods proved ineffective and inefficient for practical implementation for the next generation of mobile systems in which higher traffic demand was expected.…”