Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network

WANG Ji; LI Zilong; XIAO Jian; LI Huanzhe; XIE Wenwu; YU Chao

doi:10.11999/JEIT231186

Volume 46 Issue 6

Jun. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(6): 2409-2417

WANG Ji, LI Zilong, XIAO Jian, LI Huanzhe, XIE Wenwu, YU Chao. Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2409-2417. doi: 10.11999/JEIT231186

Citation:

WANG Ji, LI Zilong, XIAO Jian, LI Huanzhe, XIE Wenwu, YU Chao. Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2409-2417. doi: 10.11999/JEIT231186

Citation:

WANG Ji, LI Zilong, XIAO Jian, LI Huanzhe, XIE Wenwu, YU Chao. Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2409-2417. doi: 10.11999/JEIT231186

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Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network

doi: 10.11999/JEIT231186

1.
College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
2.
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China

Funds: The National Natural Science Foundation of China (62101205, 62372070), The Key Research and Development Program of Hubei Province (2023BAB061), The Research and Innovation Projects in HunanProvince (QL20230275, CX20231220), Hunan Provincial Natural Science Foundation (2023JJ50045), Hunan Provincial College Students’ Innovation and Entrepreneurship Projects (S202310543040)

Received Date: 2023-10-31
Rev Recd Date: 2024-03-15

Available Online: 2024-04-01

Publish Date: 2024-06-30

Abstract

Abstract

A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access (NOMA) systems, which can avoid the required expensive signaling overhead in successive interference cancellation algorithms, especially for NOMA-based short packet transmission. Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network, the original convolutional network is replaced by the adder network. The modulation detection accuracy, computing delay and energy consumption are fully compared for two kinds of network architectures. Meanwhile, time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio. Finally, the influence of power allocation and data packet length on detection performance is analyzed and verified.
- Non-Orthogonal Multiple Access (NOMA),
- Modulation detection,
- Deep adder network,
- Oversampling

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References(29)

References

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