An Effective Wireless Communication Architecture Using Ultra Lightweight Automatic Modulation Classification Model Over Cortex M4 Based Edge Device

An Effective Wireless Communication Architecture Using Ultra Lightweight Automatic Modulation Classification Model Over Cortex M4 Based Edge Device

연구 분야: Infrastructure

학회: 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

The future advancement in computation, IoT and ultra-edge computing devices will require support of efficient communication methods. The presented research focuses on two parts. The first one focuses on a novel automatic modulation classification (AMC) technique developed using 1D-Convolutional Neural Network useful for STM32L4 series. The approach effectively balances performance with the stringent energy and computational constraints of the ultra-edge devices. The approached has shown 95.42% reduction in Memory requirement while achieving high classification accuracy of 79.01% along with having 99.63% at high SNR (10dB to 20dB), 92.90% at medium SNR (0dB to 10dB), and 44.78% at low SNR (-10dB to 0dB). The second part highlights a combination of multiple modulation schemes in a sequential manner, which is improvising the security of wireless communication. The model also demonstrates robust performance against noise and other channel impairments. This research provides a scalable, efficient AMC solution for ultra-edge devices, enhancing the practicality and security of intelligent communication systems. By optimising for the STM32L4 platform, this work represents a significant advancement in deploying advanced signal processing techniques on resource constrained devices.