“Artificial Neural Network System for Optical communication System : A sequential Study.”

Abstract

We investigate the risk of overestimating the performance gain when applying neural network based receivers in systems with pseudo random bit sequences or with limited memory depths, resulting in repeated short patterns. We show that with such sequences, a large artificial gain can be obtained which comes from pattern prediction rather than predicting or compensating the studied channel/phenomena. Since using additive white gaussian noise (AWGN) is common to every communication channels, which is the statistically random radio noise characterized by a wide frequency range with regards to a signal in the communications channel. this assignment describes two aspects of telecommunications engineering: i) the basic understanding of MATLAB, and ii) the effect of additive white gaussian noise (AWGN) on the transmitted data using based-band simulation under different values of signal-to-noise (snr) ratio with this tool. in my experiment, I use MATLAB tools to generate the noise sequence, the data sequence, the both data and noise sequence and add them together, recover signal from noisy received data and calculate noise power, calculate SNR and BER and then plot them with different snr values.