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Block-based fuzzy step size NLMS algorithms for subband adaptive channel equalisation

Block-based fuzzy step size NLMS algorithms for subband adaptive channel equalisation

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Judicious selection of the step size parameter is crucial for adaptive algorithms to strike a good balance between convergence speed and misadjustment. The fuzzy step size (FSS) technique has been shown to improve the performance of the classical fixed step size and variable step size (VSS) normalised least mean square (NLMS) algorithms. The performance of the FSS technique in the context of subband adaptive equalisation is analysed and two novel block-based fuzzy step size (BFSS) strategies for the NLMS algorithm, namely fixed block fuzzy step size (FBFSS) and adaptive block fuzzy step size (ABFSS) are proposed. By exploiting the nature of gradient noise inherent in stochastic gradient algorithms, these strategies are shown to substantially reduce the computational complexity of the conventional FSS technique without sacrificing the convergence speed and steady-state performance. Instead of updating the step size at every iteration, the proposed techniques adjust the step size based on the instantaneous squared error once over a block length. Design methodology and guidelines that lead to good performance for the algorithms are given.

Inspec keywords: gradient methods; telecommunication channels; fuzzy set theory; stochastic processes; convergence; least mean squares methods; adaptive equalisers

Other keywords: subband adaptive channel equalisation; convergence speed; FSS balance; stochastic gradient algorithms; block-based fuzzy step size NLMS algorithms; normalised least mean square algorithms

Subjects: Communication channel equalisation and identification; Other topics in statistics; Interpolation and function approximation (numerical analysis); Combinatorial mathematics

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