Sensor-Centric Link Adaptation and Transmit Power Control for Energy-Efficient and Quality-of-Service WBANs

Due to the imbalanced power supply and computing capabilities between sensors and the coordinator in Wireless Body Area Networks (WBANs), challenges arise in terms of shortening service duration and increased delay for computation and data transmission tasks. To address this issue while meeting Quality of Service (QoS) requirements, Transmit Power Control (TPC) and Link Adaptation (LA) techniques, are applied to determine transmit power level and modulation and coding schemes (MCS). The proposed reinforcement learning algorithm leverages latent path loss and shadowing (PLS) and Signal-to-Noise Ratio (SNR), and transmit power to effectively exploit the corresponding transmit power level and MCS, enabling efficient learning. It aims to minimize energy consumption while satisfying target QoS requirements, including data rate and error ratio. The results demonstrate that our method achieves significant power savings, with transmit energy reductions of up to 11.1%, and extend sensors operating time by up to 22.4 % compared to existing methods.