Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System

This paper presents an adaptive-slope squashing-function (ASF)-based artificial neural network (ANN) for efficient estimation of smoothly time-varying multipath fading channels, in a \(4 \times 1\) space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system using 64 subcarriers. The channel-state-information (CSI) estimated at first stage is further used for OFDM information symbol detection (through minimum mean square error criterion-based detection) at second stage. To combat the impact of smoothly time-varying environment, we emphasize on the utilization of ASF-ANN using backpropagation (BP) algorithm for the estimation of channel tap coefficients in frequency domain. The underlying ANN is modeled as feedforward multi-layered perceptron that updates the network weights. The major focus is on the gradient-descent algorithm-based adaptation of the slope of squashing-function (SF) along with other ANN parameters, which enhances the training efficiency of ASF-ANN in terms of the lower mean-squared channel estimation error in comparison with the traditional fixed-slope squashing-function (FSF) ANN technique. Simulation results corresponding to the underlying \(4 \times 1\) SFBC-OFDM system are presented to depict that ASF-ANN-based approach outperforms the FSF-ANN technique by providing lower bit-error-rate (BER) due to the usage of well-estimated CSI. At 15 dB SNR and fade rate = 0.001, the average BER reduces to \(2.85 \times 10^{ - 4}\) for the ASF-ANN based approach, due to improved CSI estimation, which accounts for approximately 5% improvement in the detection success rate as compared to the FSF-ANN-based approach.