Machine Learning Based Channel Estimation for Low-Power Internet of Things (LP-IoT) Devices: A Probabilistic Analysis

The swift expansion of Low-Power Internet of Things (LP-IoT) devices has significantly impacted industries such as smart homes, healthcare, agriculture, and industrial automation. In these interconnected environments, reliable wireless communication is essential for efficient data transmission. Channel estimation techniques play a crucial role in assessing the state of the wireless channel before transmitting data. While conventional techniques have been developed, they often struggle in dynamic environments due to substantial computational demands and limited adaptability. Recent advancements in Machine Learning (ML) offer promising improvements in wireless channel estimation for LP-IoT by capturing complex relationships and adapting to changing conditions of wireless channel. Considering the ML models as the potential substitute in the field of wireless channel estimation, this paper builds on our previous work by providing a detailed analysis of two advanced ML-based models, which demonstrates their applicability and reliability in practical indoor environments. While these models have shown potential, a comprehensive analysis of their accuracy in expanded environments has been lacking. To address this gap, we conduct a probability analysis to evaluate the models’ estimation accuracy and confidence levels, alongside a scalability analysis to assess performance as network size and complexity grow. The results confirm the effectiveness of these ML-based models and provide valuable insights into their suitability for large-scale LP-IoT applications. Ultimately, this study contributes to the advancement of intelligent LP-IoT communication systems by bridging the gap between theoretical research and real-world deployment.