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Deep Learning Approach for Efficient 5G LDPC Decoding in IoT
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DOI：10.1109/ACCESS.2024.3472466.png)
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The tremendous progress of 5G technology has transformed the landscape of the Internet of Things (IoT), allowing for fast data speeds, low delay, and widespread connection that is crucial for a variety of applications, including smart cities and industrial automation. In the context of 5G enabled IoT networks, colored noise introduces varying levels of interference across different frequency bands, which can significantly degrade the performance of 5G LDPC decoding. This paper presents a novel Deep learning approach for 5G channel LDPC code decoding tailored for next-generation IoT applications. The proposed method integrates an Iterative Normalized Min-Sum (NMS) algorithm with a Convolutional Neural Network (CNN) to enhance the performance of LDPC decoding in the presence of colored noise, a common interference in real-world communication channels. Through extensive simulations and analysis, our approach demonstrates a significant performance improvement, achieving a 3.8 dB enhancement at a Bit error rate of 10(-6). This is achieved by accurately estimating and mitigating channel noise, thereby ensuring reliable data transmission for critical IoT applications. The findings indicate that our approach to decoding technique not only enhances error correction capabilities but also adapts to varying channel conditions, optimizing IoT network performance and efficiency. This research contributes a robust solution to the challenges posed by colored noise in 5G-enabled IoT networks, promoting the deployment of more reliable and efficient IoT systems.
Keyword:
Internet of Things
5G mobile communication
Colored noise
Codes
Iterative decoding
Reliability
Covariance matrices
Correlation
White noise
Vectors
Channel coding
Parity check codes
Convolutional neural networks
error correcting codes
fifth-generation (5G)
low-density parity-check (LDPC) codes
convolutional neural network (CNN)
colored noise
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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