Deep Learning Based Channel Estimation in Data Driven MIMO Receiver

OFDM (orthogonal frequency division multiplexing) is a wireless network methodology that sends multiple data streams across a particular channel while effectiently handling inter-symbol interference and enhancing frequency band available. And since the antenna is sending signals, evaluating the noise in a noisy channel is essential. This research aims into compressed sensing (CS) as a way to improve throughput and BER performance by transmitting additional data bits within every subcarrier frame whilst still limiting detector unpredictability. The Neuro-LS methodology is used in this study to generate a soft trellis decoding algorithm through channel estimation. Trellis decoding performs better BER, and DNN relying channel estimation outperforms BER, according to the findings