Self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Conditional distribution learning with neural networks and itsapplication to channel equalization
IEEE Transactions on Signal Processing
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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High power amplifier (HPA) is a main source of introducing nonlinear distortions in an orthogonal frequency division multiplexing (OFDM) systems. Methods of improving the power efficiency of a nonlinear amplifier include various amplifier linearization techniques. However amplifier linearization is expensive, power inefficient and HPA dependent. Traditional equalizers not well perform in reduction of bit error rate in presence of such nonlinear distortions in OFDM systems. This paper presents a new method of HPA independent adaptive nonlinear distortion compensator for uncoded OFDM signals, which operates at the OFDM receiver end. The compensator is based on a cascade version of a functional-link neural network (FLNN) and a self-organizing map (SOM), which compensates the HPA nonlinearities with memory effect. Equalization is done in frequency domain for each subcarrier separately. Finally we simulate the proposed system in a time dispersive channel with the effect of ISI and with the HPA nonlinearity. Simulation results show the validity of the proposed equalizer.