A Neural Encoded Fuzzy Logic Algorithm

Abstract

This chapter proposes a new class of handoff algorithms that adapts the parameters of a handoff algorithm using a neural encoded fuzzy logic system. Known sensitivities of handoff parameters can be used to design an FLS, which can then be used to adapt the handoff parameters to obtain improved performance in a dynamic cellular environment. However, the FLS has large storage requirements and high computational complexity. This chapter proposes neural encoding of the FLS to circumvent these demands; a neural network learns how the FLS works. Several neural network paradigms such as a multilayer perceptron and a radial basis function network can be universal approximators. The input-output mapping capability and compact data representation capability of neural networks are exploited here to derive an adaptive handoff algorithm that retains the high performance of the original fuzzy logic based algorithm and that has an efficient architecture for storage and computational requirements.