An Evolutionary Algorithm for Improved Diversity in DSL Spectrum Balancing Solutions

There are many spectrum balancing algorithms to combat the deleterious impact of crosstalk interference in digital subscriber lines (DSL) networks. These algorithms aim to find a unique operating point by optimizing the power spectral densities (PSDs) of the modems. Typically, the figure of merit of this optimization is the bit rate, power consumption or margin. This work poses and solves a different problem: instead of providing the solution for one specific operation point, it finds a set of operating points, each one corresponding to a distinct matrix with PSDs. This solution is useful for planning DSL deployment, for example, helping operators to conveniently evaluate their network capabilities and better plan their usage. The proposed method is based on a multiobjective formulation and implemented as an evolutionary genetic algorithm. Simulation results show that this algorithm achieves a better diversity among the operating points with lower computational cost when compared to an alternative approach.

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An Evolutionary Algorithm for Improved Diversity in DSL Spectrum Balancing Solutions

Semantic Scholar · Computer Science · 2010

Abstract

There are many spectrum balancing algorithms to combat the deleterious impact of crosstalk interference in digital subscriber lines (DSL) networks. These algorithms aim to find a unique operating point by optimizing the power spectral densities (PSDs) of the modems. Typically, the figure of merit of this optimization is the bit rate, power consumption or margin. This work poses and solves a different problem: instead of providing the solution for one specific operation point, it finds a set of operating points, each one corresponding to a distinct matrix with PSDs. This solution is useful for planning DSL deployment, for example, helping operators to conveniently evaluate their network capabilities and better plan their usage. The proposed method is based on a multiobjective formulation and implemented as an evolutionary genetic algorithm. Simulation results show that this algorithm achieves a better diversity among the operating points with lower computational cost when compared to an alternative approach.

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