Event Details
Multi-Objective Optimization for Energy-Efficient and Reliable Transmission in WBANs: Session-Specific Design Using MODRL
Presenter: Shuang Li
Supervisor:
Date: Fri, July 4, 2025
Time: 14:00:00 - 15:00:00
Place: Zoom - see below.
ABSTRACT
Abstract: In wireless body area networks (WBANs), ensuring energy efficiency while maintaining reliable transmission poses a significant and inherently conflicting design challenge. This work introduces a multi-objective optimization (MOO) design for wireless transmission in the finite blocklength (FBL) regime within a WBAN environment, aimed at minimizing both energy consumption and packet error rate (PER) for each transmission session. An intelligent agent is trained to determine near-optimal transmission parameter values for different user preferences. Specifically, the MOO problem is reformulated as a multi-objective Markov decision process (MOMDP), and a multi-objective deep reinforcement learning (MODRL)-based solution is developed to approximate the Pareto front, where the penalty-based boundary intersection (PBI) approach is utilized for reward scalarization. Simulation results demonstrate that the proposed solution effectively adapts transmission parameters to balance energy efficiency and reliability according to user preferences. Furthermore, compared to linear scalarization, the PBI-based approach provides a more complete Pareto front.
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