The combination of MIMO technology with OFDM system, there is enhancement of wireless digital communication which is quite beneficial for future communicating system. MIMO-OFDM improves the efficiency and quality of the wireless scenario. With efficient channel estimation technique especially non-blind under MIMO-OFDM scenario present an enhanced performance with low complexity. In the pilot type, the least squares method (LS method) is less complex and requires an implicit knowledge of the channels. However, it suffers from inter-carrier interference (ICI). For this reason, the optimal design of Channel Estimator is an area of ongoing research. In this work, the performance of the DWT-OFDM scheme in combination with a multi-input system and multiple outputs for unknown pilot symbols is evaluated using a neural network. Take advantage of time series prediction using a recurrent neural network (RNN) with a SoftMax layer with frequency index modulation to perform channel prediction. In this research work a comparative analysis is performed among proposed softmax recurrent neural network based channel estimation with existing channel estimation technique for variable signal to noise ratio (Eb/No) for frequency indexed modulation techniques. The existing channel estimation techniques for MIMO-OFDM communicating environment are based on known pilots over the noisy fading wireless environment. From simulation result, it is observed and concluded that the existing channel estimation techniques gives higher BER as compared to proposed softmax recurrent neural network channel estimation technique.
Recurrent Neural Network Channel Estimation Using Measured Massive MIMO Data
