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

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 c...

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Bibliographic Details
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
Online Access:Request full text
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Summary: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.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10278753