Blind Fuzzy Adaptation Step Control for a Concurrent Neural Network Equalizer

Abstract

Mobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizer.

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

“…However, some engineering problems are intrinsically dependent on complex-valued signals (e.g., channel equalization and beamforming). In order to circumvent this limitation, ANN algorithms based on complex numbers have already been proposed for some applications, such as channel equalization [5][6][7] and adaptive beamforming for wireless receivers [8][9][10][11].…”

“…Digital communication systems over wireless channels may suffer severe signal distortions due to multipath propagation, additive white Gaussian noise (AWGN) [12,13], Doppler effects and, not infrequently, nonlinearities at the receiver front-end and at the transmitter high power amplifier [5,7]. Since nonlinear impairments usually worsen the performance of linear channel equalizers, nonlinearities in the channel are better dealt with using robust nonlinear equalizers [5,7,14].…”

“…Digital communication systems over wireless channels may suffer severe signal distortions due to multipath propagation, additive white Gaussian noise (AWGN) [12,13], Doppler effects and, not infrequently, nonlinearities at the receiver front-end and at the transmitter high power amplifier [5,7]. Since nonlinear impairments usually worsen the performance of linear channel equalizers, nonlinearities in the channel are better dealt with using robust nonlinear equalizers [5,7,14]. In this context, based on the phase transmittance radial basis function neural network (PTRBFNN) equalizer [7], a blind fuzzy controller algorithm was applied to increase the concurrent neural network equalizer (CNNE) convergence speed and decrease the residual mean squared error (MSE) [5].…”

“…Since nonlinear impairments usually worsen the performance of linear channel equalizers, nonlinearities in the channel are better dealt with using robust nonlinear equalizers [5,7,14]. In this context, based on the phase transmittance radial basis function neural network (PTRBFNN) equalizer [7], a blind fuzzy controller algorithm was applied to increase the concurrent neural network equalizer (CNNE) convergence speed and decrease the residual mean squared error (MSE) [5]. Another equally important technique is the butterfly neural equalizer (BNE) which, when applied to optical communications with two-dimensional digital modulation, is able to mitigate nonlinearities in the photo-electric converters and simultaneously compensate chromatic and polarization mode dispersions [6].…”

Nonlinear Modified Concurrent Equalizer

“…Techniques equally important have been proposed, aiming to improve the performance of blind equalizers [5], [18]- [20]. In [5], the authors applied a blind fuzzy controller algorithm to increase the equalizer convergence speed and decrease the residual MSE. A different solution was proposed in [19].…”