A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems

Chen, Zikang; Ge, Wenping; Chen, Juan; He, Jiguang; He, Hongliang

doi:10.3390/e25111514

Open AccessArticle

A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems

by

Zikang Chen

^1,2

,

Wenping Ge

^1,2,*,

Juan Chen

¹,

Jiguang He

^3,4

and

Hongliang He

⁵

¹

College of Computer Science and Technology, Xinjiang University, Urumqi 830046, China

²

Signal Detection and Processing Key Laboratory, Urumqi 830046, China

³

Technology Innovation Institute, Abu Dhabi P.O. Box 9639, United Arab Emirates

⁴

Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland

⁵

College of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China

^*

Author to whom correspondence should be addressed.

Entropy 2023, 25(11), 1514; https://doi.org/10.3390/e25111514

Submission received: 7 October 2023 / Revised: 27 October 2023 / Accepted: 3 November 2023 / Published: 4 November 2023

(This article belongs to the Special Issue Advances in Information and Coding Theory II)

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Abstract

The introduction of sparse code multiple access (SCMA) is driven by the high expectations for future cellular systems. In traditional SCMA receivers, the message passing algorithm (MPA) is commonly employed for received-signal decoding. However, the high computational complexity of the MPA falls short in meeting the low latency requirements of modern communications. Deep learning (DL) has been proven to be applicable in the field of signal detection with low computational complexity and low bit error rate (BER). To enhance the decoding performance of SCMA systems, we present a novel approach that replaces the complex operation of separating codewords of individual sub-users from overlapping codewords using classifying images and is suitable for efficient handling by lightweight graph neural networks. The eigenvalues of training images contain crucial information, such as the amplitude and phase of received signals, as well as channel characteristics. Simulation results show that our proposed scheme has better BER performance and lower computational complexity than other previous SCMA decoding strategies.

Keywords: sparse code multiple access (SCMA); deep learning (DL); signal detection; bit error rate (BER)

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MDPI and ACS Style

Chen, Z.; Ge, W.; Chen, J.; He, J.; He, H. A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems. Entropy 2023, 25, 1514. https://doi.org/10.3390/e25111514

AMA Style

Chen Z, Ge W, Chen J, He J, He H. A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems. Entropy. 2023; 25(11):1514. https://doi.org/10.3390/e25111514

Chicago/Turabian Style

Chen, Zikang, Wenping Ge, Juan Chen, Jiguang He, and Hongliang He. 2023. "A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems" Entropy 25, no. 11: 1514. https://doi.org/10.3390/e25111514

APA Style

Chen, Z., Ge, W., Chen, J., He, J., & He, H. (2023). A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems. Entropy, 25(11), 1514. https://doi.org/10.3390/e25111514

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Novel Image-Classification-Based Decoding Strategy for Downlink Sparse Code Multiple Access Systems

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