| Summary: | 碩士 === 元智工學院 === 電機與資訊工程研究所 === 84 === In adaptive antenna arrays, the combination of array signals is
usually referred to as "beamforming". The goal of beamforming is
to maximize the directionalgain of the desired signal and
attenuate the interference and noise arriving from other
directions. The output of a conventional beamformer is the
linear combination of the array received signals. If the system
contains nonlinear terms or the signals are non-Gaussian,
however, optimal solutions cannot be obtained by linearly
combining the received signals. In this thesis, we use two kinds
of fuzzy adaptive networks ---- CANFIS(Complex Adaptive-Network-
Based Fuzzy Inference System) and CFBFN(Complex Fuzzy Basis
Function Network) as the nonlinear beamforming algorithms, and
compare the performance with the linear beamformer using RLS(
Recursive Least Square) method for parameter tuning both in the
linear and nonlinear signal environment. We consider a wireless
digital mobile communication system and use equivalent baseband
QPSK signal for simulation. The beamformer parameters are
trained by temporal reference signals. From the simulation
results, the performance of the fuzzy adaptive networks is
similar to that of the linear beamformer in the linear
environment, and is superior in the nonlinear environment. Fuzzy
nonlinear beamformers can better explore the spatial diversity
of antenna arrays, especially when system nonlinearityis
present.
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