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ZCLoRa: A Lightweight Deep Learning Based Receiver for Enhanced LoRa Decoding
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DOI：10.1109/LCOMM.2025.3544893.png)
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Abstract
En 中文
This letter introduces Zak-CNN LoRa (ZCLoRa), a novel receiver designed for low-power, long-range Internet of things (IoT) applications, addressing key limitations in conventional LoRa communication systems. In existing LoRa implementations, challenges such as low transmission power, unknown channel conditions, and degraded received signals lead to suboptimal decoding performance. To overcome these issues, we propose ZCLoRa, which leverages the Zak transform and exploits its sparse representation capabilities, enabling effective signal denoising. The Zak-transformed signal is processed by a convolutional neural network (CNN) for enhanced symbol decoding. We evaluate the performance of ZCLoRa in terms of symbol error rate (SER), showing significant improvements in decoding accuracy while maintaining the low complexity and power efficiency essential for IoT devices. Furthermore, we validate our model on USRPs, providing empirical evidence of its real-world effectiveness.
Keywords:
Transforms
LoRa
Symbols
Decoding
Fading channels
Noise
Internet of Things
Convolutional neural networks
Modulation
Signal to noise ratio
LoRa modulation
Zak transform
sparse representation
convolutional neural networks
USRP
Journal
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