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Multi-Encoder Semantic Communication for Human Digital Twin Synchronization

Title:

Multi-Encoder Semantic Communication for Human Digital Twin Synchronization

Talabi, Oluwasegun (2024) Multi-Encoder Semantic Communication for Human Digital Twin Synchronization. Masters thesis, Concordia University.

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Abstract

Human Digital Twin (HDT) concept introduces an innovative framework for creating digital
counterparts of individuals, enabling real-time synchronization between the physical twin (PT) and
the virtual twin (VT). This PT-VT synchronization underpins various human-centered services but
demands significant data processing and efficient communication resource allocation, particularly
in resource-constrained environments. Semantic communication has emerged as a promising al-
ternative to traditional data-driven methods; however, single-encoder models struggle to meet the
diverse and dynamic requirements of HDT applications.
To address these challenges, this thesis presents a multi-encoder semantic communication frame-
work that adaptively allocates resources—such as bandwidth, computational power, and processing
frequency—based on application-specific needs. The short-term optimization problem is formulated
as a mixed-integer nonlinear programming (MINLP) problem and solved using a genetic algorithm
(GA). Simulation results demonstrate that the proposed multi-encoder model significantly improves
synchronization quality and power efficiency, outperforming traditional single-encoder models in
terms of accuracy, latency, and resource utilization.
To achieve a balance between immediate performance and long-term objectives (e.g., queue sta-
bility, sustainable energy usage, and high throughput), the thesis explores the long-term optimiza-
tion problem, formulated as a Markov Decision Process (MDP) and solved using Lyapunov-assisted
deep reinforcement learning. This adaptable and scalable approach positions the multi-encoder se-
mantic communication model as a highly efficient solution for future HDT applications.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Talabi, Oluwasegun
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:December 2024
Thesis Supervisor(s):Cai, Jun
Keywords:Human Digital Twin Multi-Encoder Semantic Communication Mixed Integer Non-linear Problem Genetic Algorithm Multistage Stochastic Optimization Problem Lyapunov Assisted Deep Reinforcement Learning
ID Code:994928
Deposited By: Oluwasegun Talabi
Deposited On:17 Jun 2025 17:29
Last Modified:17 Jun 2025 17:29

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