Channel estimation using an adaptive neuro fuzzy inference system in the OFDM-IDMA system

In this paper, a channel estimator based on an adaptive neuro fuzzy inference system (ANFIS) is proposed for the purpose of estimating channel frequency responses in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems. To see the performance of our proposed channel estimation method, five different techniques including well-known pilot-based estimation algorithms such as least squares (LS) and minimum mean square error (MMSE) with other heuristic methods like multilayered perceptron (MLP) trained by a backpropagation (BP) algorithm (MLP-BP), MLP trained by the Levenberg-Marquardt (LM) algorithm (MLP-LM), and radial basis function neural network (RBFNN) are compared with our proposed method by computer simulations. The comparisons are made with the aid of bit error rate and mean square error graphs. According to the simulation results, our proposed channel estimator based on ANFIS shows better performance than both the LS algorithm and the other considered heuristic methods like MLP-BP, MLP-LM, and RBFNN, whereas the MMSE algorithm still shows the best performance as expected because of exploiting channel statistics and noise information, which makes it very complex to be used in any system. As well as being less complex compared to the MMSE algorithm, the estimator based on ANFIS does not need pilot tones for channel estimation. These properties bring our proposed method to an advantageous position among the other estimation techniques.