Low Complexity Deep Learning-Assisted Golden Code Sphere-Decoding with Sorted Detection Subsets
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作者:
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机构:
Mthethwa, Bhekisizwe
[1
]
Xu, Hongjun
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机构:
Univ KwaZulu Natal, Sch Engn, ZA-4041 Durban, South AfricaUniv KwaZulu Natal, Sch Engn, ZA-4041 Durban, South Africa
Xu, Hongjun
[1
]
机构:
[1] Univ KwaZulu Natal, Sch Engn, ZA-4041 Durban, South Africa
来源:
SAIEE AFRICA RESEARCH JOURNAL
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2022年
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113卷
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02期
关键词:
Alamouti;
deep learning;
Golden code;
spacetime;
coding;
sphere-decoding;
TRANSMIT DIVERSITY;
PERFORMANCE;
D O I:
10.23919/SAIEE.2022.9785542
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
Golden code is a space-time block coding (STBC) scheme that has spatial multiplexing gain over the Alamouti STBC which is widely used in modern wireless communication standards. Golden code has not been widely adopted in modern wireless standards because of its inherent high detection complexity. However, detection algorithms like the sphere-decoding with sorted detection subsets (SD-SDS) have been developed to lower this detection complexity. Literature indicates that the SD-SDS algorithm has lower detection complexity relative to the traditional sphere-decoding (SD) algorithm, for all signalto-noise ratio (SNR) values. The SD-SDS algorithm exhibits low detection complexity at high SNR; however, at low SNR the detection complexity is higher. We propose a deep neural network (DNN) aided SD- SDS algorithm (SD-SDSDNN) that will lower the Golden code's SD-SDS low SNR detection complexity, whilst maintaining the bit-error-rate (BER) performance. The proposed SD-SDS-DNN is shown to achieve a 75% reduction in detection complexity relative to SD-SDS at low SNR values for 16-QAM, whilst maintaining the BER performance. For 64-QAM, the SDSDS- DNN achieves 99% reduction in detection complexity relative to the SD- SDS at low SNR, whilst maintaining the BER performance. The SD-SDS-DNN has also shown to achieve low detection complexity comparable to that of the Alamouti linear maximum likelihood (ML) detector for a spectral efficiency of 8 bits/s/Hz. For a spectral efficiency of 12 bits/s/Hz, the SD-SDS-DNN achieves a detection complexity that is 90% lower than the Alamouti linear ML detector.
机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Sun Yat sen Univ, Guangdong Prov Key Lab Environm Pollut Control & R, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Sun, Lianpeng
Zhu, Jinjun
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Zhu, Jinjun
Tan, Jinxin
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Tan, Jinxin
Li, Xianfeng
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机构:
Macau Univ Sci & Technol, Sch Comp Sci & Engn, Taipa, Macau, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Li, Xianfeng
Li, Ruohong
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Sun Yat sen Univ, Guangdong Prov Key Lab Environm Pollut Control & R, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Li, Ruohong
Deng, Huanzhong
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Deng, Huanzhong
Zhang, Xinyang
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Zhang, Xinyang
Liu, Bingyou
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Liu, Bingyou
Zhu, Xinzhe
论文数: 0引用数: 0
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机构:
Sun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China
Sun Yat sen Univ, Guangdong Prov Key Lab Environm Pollut Control & R, Guangzhou 510275, Peoples R ChinaSun Yat sen Univ, Sch Environm Sci & Engn, Guangzhou 510275, Peoples R China