Deep Learning-Based Joint Channel Coding and Frequency Modulation for Low Power Connectivity

• Main Authors: Chang, Boxuan, Wang, Chenyu, Kim, Hun-Seok
• Format: Conference Proceeding
• Language: English
• Subjects: Deep Learning; Digitally Controlled Oscillator; Frequency modulation; GRU; mMTC; Phase modulation; Phase noise; Power demand; Receivers; RF signals; Wireless communication
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10278753

Low-power, low-cost wireless communication is a fundamental requirement of Internet-of-Things (IoT) and massive machine-type communication (mMTC). Various low power connectivity standards such as Bluetooth and LoRa adopt non-coherent frequency modulation schemes as they exhibit significantly lower complexity and power consumption compared to coherent in-phase and quadrature (IQ) modulation schemes. In our paper, we propose a deep learning-based joint channel coding and modulation (JCM) scheme for digitally controlled oscillator (DCO)-based frequency modulation. The learned encoder takes an information bit sequence and produces DCO control samples that represent instantaneous frequency to modulate the radio frequency (RF) signal. The learned decoder recovers/decodes information bits from the received noisy samples without any preamble to assist time and frequency synchronization. We train and test the proposed scheme under significant phase noise and carrier frequency offset (CFO) of the DCO to successfully mitigate these practical impairments at the receiver.