Towards sustainable and realtime wireless body area networks: Smart scheduling and session-specific design with deep reinforcement learning
| dc.contributor.author | Li, Shuang | |
| dc.contributor.supervisor | Yang, Hong-Chuan | |
| dc.date.accessioned | 2025-08-15T21:07:16Z | |
| dc.date.available | 2025-08-15T21:07:16Z | |
| dc.date.issued | 2025 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Doctor of Philosophy PhD | |
| dc.description.abstract | Wireless body area networks (WBANs) are essential for real-time health monitoring and control but face key challenges: (1) sustaining energy efficiency under limited battery capacity, (2) ensuring information freshness by delivering timely and reliable status updates, and (3) managing conflicting performance requirements, such as energy consumption and information freshness. To address these challenges, this thesis presents a series of optimal transmission designs for WBANs leveraging machine learning (ML) and deep reinforcement learning (DRL) technologies. First, we investigate energy-efficient transmission designs that minimize energy consumption for each transmission session. Second, we develop joint scheduling and transmission design strategies to minimize the age of information (AoI). Third, we propose multi-objective optimization (MOO) approaches to balance trade-offs among conflicting performance metrics. For each transmission design, we formulate the corresponding optimization problem as a Markov Decision Process (MDP). Based on the characteristics of the state and action spaces, we develop tailored ML/DRL-based solutions to learn near-optimal transmission policies. We highlight key trade-offs in transmission design and conduct comparative analyses through selected numerical examples. The results demonstrate the effectiveness of the proposed ML/DRL-based solutions in addressing complex transmission designs in WBANs, enabling energy-efficient and freshness-sensitive communication, and advancing the practical deployment of sustainable and real-time health monitoring systems. | |
| dc.description.embargo | 2026-08-05 | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.bibliographicCitation | S. Li, H. -C. Yang and F. Hu, “Joint Transmission Mode Selection and Scheduling for AoI Minimization in NOMA-Capable WP-IoT Networks: A Deep Transfer Learning Solution,” IEEE Trans. Commun., Early Access, 2025. | |
| dc.identifier.bibliographicCitation | S. Li, H. -C. Yang, F. Xu, H. Hu and F. Hu, “Energy-Efficient Relay Transmission for WBAN: Energy Consumption Minimizing Design with Hybrid Supervised/ Reinforcement Learning,” IEEE Internet Things J., vol. 11, no. 10, pp. 17770-17779, May 15, 2024. | |
| dc.identifier.bibliographicCitation | S. Li, H. Yu, and H. -C. Yang, “Multi-Objective Optimization for Energy-Efficient and Reliable Transmission in WBANs: Session-Specific Design Using MODRL,” IEEE VTC2025-Spring Workshops, Oslo, Norway, 2025. | |
| dc.identifier.bibliographicCitation | S. Li, F. Xu and H. -C. Yang, “Reliable and Energy-Efficient Relay Transmission in WBANs with Wireless Power Transfer: Optimal Design with DRL,” IEEE PacRim, Victoria, BC, Canada, 2024, pp. 1-6. | |
| dc.identifier.bibliographicCitation | S. Li, H. -C. Yang and F. Hu, “Average AoI Minimization in WP-IoT Networks: Optimal Scheduling for NOMA Transmission,” IEEE/CIC ICCC, Dalian, China, 2023, pp. 1-6. | |
| dc.identifier.uri | https://hdl.handle.net/1828/22617 | |
| dc.language | English | eng |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | Wireless Body Area Networks | |
| dc.subject | Session-Specific Design | |
| dc.subject | Age of Information | |
| dc.subject | Deep Reinforcement Learning | |
| dc.title | Towards sustainable and realtime wireless body area networks: Smart scheduling and session-specific design with deep reinforcement learning | |
| dc.type | Thesis |