AI Based Gated CNN Equalizer for Fiber & FSO Optical Links

AI-Based Gated-CNN Equalizer for Fiber & FSO Optical Links in MATLAB Supercharge your optical-communications project with a deep-learning equalizer that works for fiber and free-space optical (FSO) IM/DD links. This video walks through the full MATLAB pipeline—data synthesis, training, evaluation, and plotting—using a gated 1D-CNN + squeeze-and-excitation (SE) architecture. We compare against an LMS baseline and visualize results with BER vs Es/N0 curves, confusion matrices, eye diagrams, and soft-output PDFs. What you’ll learn How to model fiber dispersion and FSO Gamma-Gamma turbulence with path loss, AWGN, and shot noise Building a sequence-to-label 1D-CNN with gated attention and SE channel re-weighting Optional focal cross-entropy + label smoothing for robust training under deep fades Generating and saving publication-ready plots automatically in MATLAB Fair, apples-to-apples DL vs LMS performance comparison across Es/N0 Chapters 00:00 Intro & goals 00:45 System model (IM/DD, RRC, impairments) 02:40 Network design (Gated-CNN + SE) 04:30 Training setup & domain randomization 06:10 Evaluation (DL vs LMS) 08:00 Graphs & results 09:30 How to switch Fiber ↔ FSO and customize M-PAM Use cases Academic projects & theses (fiber/FSO equalization) Rapid prototyping for optical receivers Benchmarks for DL-based equalizers vs adaptive filters Keywords / SEO free space optics, FSO, fiber optics, IM/DD, M-PAM, optical communication, deep learning equalizer, CNN equalizer, gated attention, squeeze and excitation, focal loss, MATLAB code, BER vs SNR, LMS baseline, turbulence, Gamma-Gamma, chromatic dispersion Hashtags #OpticalCommunication #FSO #FiberOptics #DeepLearning #MATLAB #CNN #Equalizer #BER #SignalProcessing #telecommunication Abstract — AI-Based Gated-CNN Equalizer for Fiber & FSO Optical Links (MATLAB) This project presents a deep-learning equalizer for intensity-modulation/direct-detection (IM/DD) optical links spanning both single-mode fiber and free-space optical (FSO) channels with M-PAM signaling. We model key impairments—chromatic dispersion (fiber), Gamma-Gamma turbulence and path loss (FSO), bandwidth limits, AWGN, and shot noise—and generate matched-filtered symbol streams using RRC pulse shaping. The proposed equalizer is a sequence-to-label 1D Convolutional Neural Network augmented with two mechanisms that improve robustness: (i) a gated-attention branch that learns symbol-time saliency and (ii) a squeeze-and-excitation (SE) block that re-weights feature channels. Training uses domain randomization over Es/N0 and channel parameters; an optional focal cross-entropy with label smoothing further stabilizes learning under class imbalance and deep fades. In simulation, the learned equalizer consistently outperforms a conventional LMS baseline across a practical Es/N0 range, with improvements visualized via BER-versus-Es/N0 curves, confusion matrices, and soft-output distributions. The implementation is end-to-end in MATLAB, including data synthesis, training, evaluation, and automatic plot export, and can be toggled between fiber and FSO profiles without code changes. Keywords: free-space optics, fiber optics, IM/DD, M-PAM, deep learning, gated CNN, squeeze-and-excitation, focal loss, equalization, MATLAB.