Adaptive Real-Time Channel Estimation and Parameter Adjustment for LoRa Networks in Dynamic IoT Environments.

Area of Science:

• Wireless Communication Engineering
• Machine Learning for IoT

Background:

• Dynamic Internet of Things (IoT) environments pose challenges for Long-Range (LoRa) networks, particularly in real-time channel state estimation and adaptive parameter adjustment.
• Existing methods struggle to efficiently adapt to highly variable channel conditions in LoRa networks.

Purpose of the Study:

• To develop an innovative approach for real-time channel state estimation and adaptive parameter adjustment in dynamic LoRa networks.
• To enhance the performance and reliability of LoRa communication in challenging IoT scenarios.

Main Methods:

• Utilized a hybrid feature extraction method combining statistical analysis and domain knowledge for real-time data labeling (SNR, RSSI).
• Employed an adaptive sliding window technique for efficient processing of recent data.
• Introduced a multi-task Long Short-Term Memory (LSTM) neural network with online incremental learning and a confidence measure for state prediction and reliability.

Main Results:

• Achieved a 100% packet delivery ratio and reduced energy consumption to 0.07987 Joules per packet.
• Demonstrated high prediction accuracy (97.70%–97.9%) for estimating different channel states.
• The confidence-based adaptive strategy effectively balanced performance optimization and stability.

Conclusions:

• The proposed data-driven framework offers robust real-time channel state estimation and adaptive parameter control for dynamic IoT environments.
• Significant improvements in communication reliability, adaptive control, and computational efficiency were achieved.
• The approach ensures robust LoRa network performance in dynamic IoT settings.