(untitled) (مجلة الفاروق للعلوم, 2026)
This paper reports a MATLAB/Simulink simulation study for predicting Erbium-Doped Fiber Amplifier (EDFA) gain, optical signal-to-noise ratio (OSNR), and amplified spontaneous emission (ASE) noise by using a hybrid convolutional neural network and long short-term memory (CNN-LSTM) model. The work was implemented in MATLAB R2022b. A dynamic data set was generated in MATLAB from six operating variables: input optical power, pump power, wavelength, temperature, active channel count, and span length. The trained network was saved and deployed in Simulink using a Predict block. Two Simulink models were built: an offline validation model using stored test signals and an online simulation model using runtime source blocks. The word online is used only in the Simulink simulation sense; the model is not connected to physical EDFA hardware, optical spectrum analyzers, or pump-control equipment. Output-specific evaluation produced RMSE values of 1.4581 dB for gain, 2.7702 dB for OSNR, and 1.5373 dBm for ASE noise. A rule-based AI decision-support layer was also added to generate status, pump-action, and alarm indicators. The study demonstrates a complete software workflow that can be used as a preliminary platform before measured EDFA data or hardware-in-the-loop testing are introduced