Design and optimization of transceiver design for semantic communication

Zhao, Fangzhou (2025) Design and optimization of transceiver design for semantic communication. PhD thesis, University of Glasgow.

Abstract

Semantic communication (SemCom) has emerged as a transformative paradigm that transmits the meaning of information instead of raw data, offering significant potential to break through the limitations of traditional communication systems. By focusing on semantics, SemCom can dramatically reduce data transmission requirements, enhance communication efficiency, and enable intelligent decision-making in complex environments. Its applications range from terrestrial wireless networks to extraterrestrial missions, where it can significantly reduce communication latency and ensure reliable information exchange. However, the practical deployment of SemCom requires overcoming multiple challenges to ensure its universal applicability and robust performance across diverse scenarios.

These challenges can be broadly categorized into two key areas. First, when introducing SemCom within traditional communication frameworks, selecting an appropriate semantic coding model (SCM) remains difficult due to the diversity of source information, user background knowledge (BK), and dynamic channel conditions. Efficiently managing computing resources and bandwidth is also essential, as large-scale semantic coding models consume significant resources. Furthermore, constructing effective background knowledge for reasoning in SemCom is complex, particularly when training data is insufficient or incomplete. Addressing these challenges is critical to achieving the general applicability and reliability of SemCom. Second, SemCom’s potential can be further unlocked in specific engineering applications where its advantages are particularly evident. For example, in autonomous lunar landing missions and UAV/UGV cooperative operations, SemCom’s capability to extract and transmit only the most relevant semantic information becomes essential. However, these scenarios introduce additional obstacles such as channel instability, limited computational capacity, and dynamic communication conditions. Developing tailored SemCom frameworks is necessary to ensure robust performance and reliable communication in these demanding environments.

To ensure the reliable and efficient operation of SemCom, my work proposes a series of solutions. First, a Background knowledge Aware SCM SElection (BASE) scheme is developed to tackle the SCM selection problem. BASE leverages graph theory to model relationships between different BKs and employs a deep learning algorithm to predict the performance of semantic coding models. This approach achieves higher information recovery accuracy and improves the likelihood of selecting optimal models compared to traditional methods. Second, a joint computing resource and bandwidth allocation framework is proposed to optimize resource management in SemCom networks. Formulated as a deep reinforcement learning task, this problem is addressed using a multi-agent proximal policy optimization algorithm, which maximizes semantic accuracy under resource-constrained conditions. Third, to enhance the reliability of SemCom transceivers, a GAI-assisted SemCom framework (Gen-SC) is introduced. By utilizing Generative Artificial Intelligence (GAI) to generate high-quality training samples tailored to user contexts, Gen-SC improves the reasoning capabilities of semantic coding models. A discriminator module further ensures that generated samples align with actual data distributions, enabling higher semantic accuracy, especially in scenarios with limited training data.

Building on these foundations, the effectiveness of SemCom is demonstrated in challenging application scenarios. For extraterrestrial missions, a novel SemCom framework is designed to support autonomous lunar landing. This framework facilitates the transmission of essential image features from the lander to satellites running remote landing control algorithms. By employing adaptive semantic encoding, it enhances landing accuracy, reduces end-to-end latency, and ensures robust performance in harsh lunar environments. Additionally, a control aware SemCom framework is proposed for UAV and UGV cooperative path planning. Instead of transmitting raw sensory data, this framework extracts and communicates only the critical semantic information relevant to path planning. This approach effectively addresses communication challenges caused by channel fading, interference, and occlusion. The proposed transceiver design ensures accurate and timely coordination between UAVs and UGVs, improving path planning efficiency and mission success rates. Through these contributions, my work advances the understanding and application of SemCom, providing comprehensive solutions for its practical deployment. The proposed methodologies enhance resource efficiency, ensure reliable transceiver operation, and demonstrate robust performance in both terrestrial and extraterrestrial environments. These findings offer valuable insights into the development of intelligent and resilient communication systems for future applications.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Sun, Dr. Yao
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85498
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 03 Oct 2025 14:27
• Last Modified: 03 Oct 2025 14:30
• Thesis DOI: 10.5525/gla.thesis.85498
• URI: https://theses.gla.ac.uk/id/eprint/85498

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