Data-Driven Evolutionary Multiobjective Optimization of Cell Power in LTE Networks | IEEE Conference Publication | IEEE Xplore

Data-Driven Evolutionary Multiobjective Optimization of Cell Power in LTE Networks


Abstract:

A reasonable allocation of cell power can dramatically improve LTE networks' performance. Optimization of cell power in LTE networks can be modeled as a multiobjective op...Show More

Abstract:

A reasonable allocation of cell power can dramatically improve LTE networks' performance. Optimization of cell power in LTE networks can be modeled as a multiobjective optimization problem, which considers several objectives and constrains and depends on cell configuration parameters and MRO measurement reports. Applying evolutionary algorithms to power optimization faces a difficulty that the computational cost increases sharply with the increase of the network scale, and fitness evaluation takes the most time. In order to reduce the computational cost and ensure the convergence of the optimization procedure, this paper proposed an approach of multiobjective power optimization based on data-driven evolutionary algorithms. A BP neural network is trained as the surrogate, and the generation-by-generation optimization procedure is divided into learning cycles and evaluation cycles. In learning cycles, the NN surrogate learns the optimization objectives and fitness evaluation functions through training samples. In evaluation cycles, the surrogate is used to approximately and quickly calculate the individual fitness and the fidelity of the surrogate is checked. The real-world network data is used to verify the above-mentioned method. The results show that the proposed method can effectively reduce the computational cost and improve the optimization efficiency without degrading the convergence and the solution quality of the optimization procedure.
Date of Conference: 23-25 November 2018
Date Added to IEEE Xplore: 10 March 2019
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Conference Location: Beijing, China

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