Energy-Aware NOMA-Based LoRa Framework for 5G-Enabled IoT

Low Power Wide Area Networks (LPWANs) and Non-Orthogonal Multiple Access (NOMA) are becoming increasingly important as the need for scalable and energy-efficient wireless communication systems in Internet of Things applications grows. To increase spectral efficiency while preserving low power consumption, this study proposes a hybrid architecture that combines Power-Domain NOMA (PD-NOMA) with LoRa technology using ESP32 microcontrollers. Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), and spectral efficiency are thoroughly examined by simulating PD-NOMA and LoRa in Rayleigh and Rician fading environments using MATLAB. A cloud-based IoT setup using ThingSpeak is integrated with the real-time hardware to enable continuous data logging and monitoring of power and SNR. Additionally, Telegram-based alert notifications provide real-time updates when power thresholds are exceeded or SNR sharply declines. This hybrid simulation and deployment approach ensures both analytical depth and practical adaptability. By leveraging MATLAB, ThingSpeak, and Telegram integration, the system delivers a reliable, energy-efficient communication solution verified by both simulation and real-time results, which are ideal for scalable, energy-conscious IoT deployments.