Vol.41, No.1, 2022, pp.171-185, doi:10.32604/csse.2022.019799
Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System
  • I. Kalphana1,*, T. Kesavamurthy2
1 Government College of Engineering, Salem, 636011, India
2 PSG College of Technology, Coimbatore, India
* Corresponding Author: I. Kalphana. Email:
Received 26 April 2021; Accepted 18 June 2021; Issue published 08 October 2021
Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).
Deep learning; channel estimation; multiple input multiple output; least square; linear minimum mean square error and orthogonal frequency division multiplexing
Cite This Article
Kalphana, I., Kesavamurthy, T. (2022). Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System. Computer Systems Science and Engineering, 41(1), 171–185.
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