Outdated Channel Overhead Minimization Using LSTM Based Deep Learning Approach in FSO/RF Relaying Systems

The research work invokes the long short-term memory (LSTM) deep learning model for combating the issue of outdated channel state information (CSI) during channel estimation on the wireless medium. For demonstration of the concept, the downlink free space optical (FSO)/radio frequency (RF) relaying strategy with outdated CSI has been contemplated. In the considered amplify-and-forward (AF) cooperative relaying, the channel gain has been extracted based on the available outdated CSI at the relay node. Of course, the performance of the FSO/RF downlink system is inferior due to low correlation between the previous (original) channel state and the measured CSI during the next time interval. Since the LSTM can use larger input data sequentially to predict the next target probabilities based on correlation among input variables, they become most suited for CSI estimation from the available outdated CSI. The trained LSTM model becomes accomplished to estimate the previous state, thus serving the relay node to adjust the gain more accurately. The trained LSTM model in the present research work is highly accurate with mean square error (MSE) and root mean square error (RMSE) of <inline-formula><tex-math notation="LaTeX">$MSE=-43.49$</tex-math></inline-formula> dB and <inline-formula><tex-math notation="LaTeX">$RMSE=-13.42$</tex-math></inline-formula> dB, respectively. The performance of the downlink FSO/RF relay has been presented in terms of outage probability, ergodic capacity and bit error rate (BER). It has been shown in the paper that using the trained deep learning LSTM model, the performance of the relaying system can be made equivalent to that when timing delay exists between the original and the estimated sample values.