Date of Defense
6-4-2026 10:00 AM
Location
F1-1164
Document Type
Thesis Defense
Degree Name
Master of Science in Electrical Engineering (MSEE)
College
COE
Department
Electrical and Communication Engineering
First Advisor
Dr. Mohamed Atef
Abstract
This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods.
The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally validate hybrid traditional equalization pipelines for real-time VLC systems,(ii) investigate machine-learning post-equalizers for VLC and compare them with conventional approaches,and (iii) perform a performance–complexity study of major neural-network families for SI-POF channel equalization.
The research combines analytical modeling, experimental measurements, and data-driven learning. For VLC systems, zero-forcing pre-equalization and LMS adaptive post-equalization are implemented using a commercial LED-based testbed with real-time performance evaluation. Machine-learning post-equalizers, including artificial neural networks and a proposed Binary Neural Tree (BNT) architecture, are trained on experimentally acquired data. For SI-POF systems, an emulated channel model is employed, and ANN, CNN, and LSTM-based equalizers are evaluated under varying noise and signal-to-noise ratio conditions.
Experimental results demonstrate that hybrid traditional equalization enables reliable high-speed VLC transmission with significant bandwidth extension compared to unequalized links. Machine-learning post-equalizers achieve improved bit-error-rate performance at higher data rates, with the proposed BNT equalizer providing comparable accuracy to conventional ANN models while requiring substantially lower computational cost. Simulation results on SI-POF channels show that neural-network-based equalizers exhibit different robustness and complexity trade-offs, particularly under low-SNR conditions.
This thesis provides a unified experimental and simulation-based evaluation of traditional and machine learning equalization techniques for commercial short-reach optical systems. A key contribution is the introduction and validation of the Binary Neural Tree equalizer, which achieves efficient performance– complexity trade-offs suitable for practical deployment. Additionally, the work establishes standardized performance and computational comparisons across multiple equalizer families.
The research addresses the lack of experimentally validated complexity-aware equalization studies using commercial LEDs and realistic short-reach optical channels. It bridges the gap between high-performance machine-learning equalizers and practical real-time implementation requirements for VLC and SI-POF communication systems.
Included in
TRADITIONAL AND MACHINE-LEARNING EQUALIZATION TECHNIQUES FOR BANDWIDTH-LIMITED SHORT-REACH OPTICAL COMMUNICATION CHANNELS
F1-1164
This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods.
The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally validate hybrid traditional equalization pipelines for real-time VLC systems,(ii) investigate machine-learning post-equalizers for VLC and compare them with conventional approaches,and (iii) perform a performance–complexity study of major neural-network families for SI-POF channel equalization.
The research combines analytical modeling, experimental measurements, and data-driven learning. For VLC systems, zero-forcing pre-equalization and LMS adaptive post-equalization are implemented using a commercial LED-based testbed with real-time performance evaluation. Machine-learning post-equalizers, including artificial neural networks and a proposed Binary Neural Tree (BNT) architecture, are trained on experimentally acquired data. For SI-POF systems, an emulated channel model is employed, and ANN, CNN, and LSTM-based equalizers are evaluated under varying noise and signal-to-noise ratio conditions.
Experimental results demonstrate that hybrid traditional equalization enables reliable high-speed VLC transmission with significant bandwidth extension compared to unequalized links. Machine-learning post-equalizers achieve improved bit-error-rate performance at higher data rates, with the proposed BNT equalizer providing comparable accuracy to conventional ANN models while requiring substantially lower computational cost. Simulation results on SI-POF channels show that neural-network-based equalizers exhibit different robustness and complexity trade-offs, particularly under low-SNR conditions.
This thesis provides a unified experimental and simulation-based evaluation of traditional and machine learning equalization techniques for commercial short-reach optical systems. A key contribution is the introduction and validation of the Binary Neural Tree equalizer, which achieves efficient performance– complexity trade-offs suitable for practical deployment. Additionally, the work establishes standardized performance and computational comparisons across multiple equalizer families.
The research addresses the lack of experimentally validated complexity-aware equalization studies using commercial LEDs and realistic short-reach optical channels. It bridges the gap between high-performance machine-learning equalizers and practical real-time implementation requirements for VLC and SI-POF communication systems.