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.

本文提出了一种基于自适应斜率挤压函数（ASF）的人工神经网络（ANN），用于有效估计时变多径衰落信道的\（4 \ times 1 \）使用64个子载波的空频块编码正交频分复用（SFBC-OFDM）系统。在第一阶段估计的信道状态信息（CSI）进一步用于第二阶段的OFDM信息符号检测（通过基于最小均方误差标准的检测）。为了应对平滑的时变环境的影响，我们强调了利用反向传播（BP）算法在频域中估计信道抽头系数的ASF-ANN的利用。基础的ANN被建模为可更新网络权重的前馈多层感知器。主要侧重于基于梯度下降算法的压扁函数（SF）斜率以及其他ANN参数的适应，与传统的固定坡度挤压函数（FSF）ANN技术相比，该算法在较低的均方信道估计误差方面提高了ASF-ANN的训练效率。仿真结果对应于底层提出了（4 x 1） SFBC-OFDM系统，以描述基于ASF-ANN的方法由于使用了经过良好估计的CSI而提供了较低的误码率（BER），从而优于FSF-ANN技术。在15 dB SNR和衰落速率= 0.001的情况下，由于改进的CSI估计，基于ASF-ANN的方法的平均BER降低至\（2.85 \乘以10 ^ {-4} \），这大约占5％的改善。与基于FSF-ANN的方法相比，检测成功率更高。