Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

摘要

Higher transmission rate is one of the technological features of prominentlyused wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among aneffective solution for channel estimation in wireless communication system, specificallyin different environments is Deep Learning (DL) method. This researchgreatly utilizes channel estimator on the basis of Convolutional Neural NetworkAuto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifieris one among Deep Learning (DL) algorithm, in which video signal is fed asinput by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improvedperformances are achieved by using CNNAE based channel estimation, in whichextension is done for channel selection as well as achieve enhanced performancesnumerically, when compared with conventional estimators in quite a lot of scenarios.Considering reduction in number of parameters involved and re-usability ofweights, CNNAE based channel estimation is quite suitable and properly fits tothe video signal. CNNAE classifier weights updation are done with minimized Signalto Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).