Abstract
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).
Highlights
MIMO integrated with OFDM technique is one among the eminent broadband wireless access system comprising of peculiar features such as, huge system capacity and higher data rates deprived of additional bandwidth and power consumption [1]
The entire implementation of the proposed channel estimation method in MIMO-OFDM with 8 Â 8 has been carried out in MATLAB simulation and the performance has been measured based on the parameters, such as Mean Square Error (MSE), Signal to Noise Ratio (SNR), Symbol Error Rate (SER) and Bit Error Rate (BER)
Since video signals have been exploited during the simulation of this work, they transfer an integer through the channels of Rayleigh, Rician and Nakagami by considering it as an input
Summary
MIMO integrated with OFDM technique is one among the eminent broadband wireless access system comprising of peculiar features such as, huge system capacity and higher data rates deprived of additional bandwidth and power consumption [1]. Channel estimation in a precise manner is highly necessitated for obtaining the transmitted signal through channel equalization It requires precise Channel State Information (CSI) of the system’s receiver end for transmitting signal coherent detection, which is regarded as significant challenge for achieving optimum performance of MIMO-OFDM systems. When compared to Linear Minimum Mean Square Error technique by means of suggested model-driven DL receiver, precise channel estimation is attained and higher data recovery accuracy is highly achieved. When compared with prevailing approaches and Fully Connected Deep Neural Network (FC-DNN), Robustness in terms of signal-to-noise ratio is further validated through simulation outcomes, which is superior in terms of computational complexities or memory usage compared to FC-DNN approach. DNN approach clearly explains the channel distortion and transmitted symbols which are detected with improved performance equivalent to Minimum Mean-Square Error (MMSE) estimator, validated through simulation outcome. Closed-form expression for CNNAE based channel estimation is formulated, which is considered to be highly sensitive for training data quality
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
