Cost-effective Base Station Deployment Approach Based on Artificial Immune Systems
نویسندگان
چکیده
This work presents a cost-effective base station deployment model based on artificial immune systems. It uses a multiobjective algorithm based on artificial immune systems (MOAIS) as an optimiser. MO-AIS algorithms are a new class of evolutionary algorithms. The Binary-coded Multi-objective Optimisation Algorithm (BRMOA) is inspired by the clonal selection theory and the immune network theory. In this innovative approach, the network is optimised for high service coverage and low cost. The cost function takes into account user-defined geographical costs and environmental legislation. The optimisation strategy is applied to two realistic scenarios and results are compared.
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