Fig. 21

Performance analysis of hybrid approach for EDAC AWGN Channel. As shown in Figs. 21 and 22, we apply two types of noise, Rayleigh Channel and AWGN channel, for all algorithms. AWGN Channel is considered to perform in the best possible way and the only reason to reduce the power of the channel. Rayleigh fading is considered the worst-case scenario as there is no effective way. Here, we can see that LDPC, Turbo, Convolutional, BCH, and Shannon’s Theorem and these algorithms are best performing for different types of data. Some algorithms are good for less data, and others are better for more data. LDPC works better when signal noise is increasing. Convolutional code works best when signal noise is medium, and the bit error ratio is 10⁻³. LDPC works better for the AGWN channel. We transmitted data continuously using machine learning techniques and which type of data was best for which algorithms and identified more error data and removed error data in less time