LAUSR

This paper proposes a Dual-Discriminator Conditional Generative Adversarial Network-based LDPC-Coded OFDM Free-Space OCS for Improving BER Analysis (LDPC-OFDM-FSO-DDcGAN). The proposed LDPC-OFDM-FSO-DDcGAN method containsthe transmitter model, the channel model and the receiver model. The binary data are given to the encoder of LDPC in the transmitter model and the input data encode previously controlling the situation using QAM. To decrease the effects of fading caused by turbulence, the channel model intends a probability density function with FSO-DDcGAN. The maximal ratio combing (MRC), equal gain combining (EGC) and selection combining (SC) can be employed by the combiner to integrate the signal once it has been received from the FSO channel. The combiner is used to improve one signal by combining the several signals received. The bit of data is transformed towards a sequential stream after combiner. The proposed Probability Density Function (PDF) offers high SNR gain, BER reduction and power efficiency when combined with DDcGAN. To achieve large coding gain, the standard coding methods of LDPC are used through only a code percentage as realistic to imitation data. The proposed LDPC-OFDM-FSO-DDcGAN technique is executed in MATLAB. The LDPC-OFDM-FSO-DDcGAN method provides higher power efficiency and lower bit error rate analysed with the existing models.