Research on Symbol Detection of Mixed Signals Based on Sparse AutoEncoder Detector

HAO Chongzheng; DANG Xiaoyu; LI Sai; WANG Chenghua

doi:10.11999/JEIT211074

Volume 44 Issue 12

Dec. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(12): 4204-4210

HAO Chongzheng, DANG Xiaoyu, LI Sai, WANG Chenghua. Research on Symbol Detection of Mixed Signals Based on Sparse AutoEncoder Detector[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4204-4210. doi: 10.11999/JEIT211074

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HAO Chongzheng, DANG Xiaoyu, LI Sai, WANG Chenghua. Research on Symbol Detection of Mixed Signals Based on Sparse AutoEncoder Detector[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4204-4210. doi: 10.11999/JEIT211074

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Research on Symbol Detection of Mixed Signals Based on Sparse AutoEncoder Detector

doi: 10.11999/JEIT211074

College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Funds: The National Natural Science Foundation of China (62031017, 61971221), The Fundamental Research Funds for the Central Universities of China (NP2020104)

Received Date: 2021-10-08
Accepted Date: 2022-03-01
Rev Recd Date: 2022-02-28

Available Online: 2022-03-09

Publish Date: 2022-12-16

Abstract

Abstract

The architecture of Deep Neural Network (DNN) based detectors can affect the Symbol Detection (SD) accuracy and computational complexity. However, most of the works ignore the architecture selection method when establishing a DNN-based symbol detector. Moreover, the existing DNN detectors use complex architectures and only perform single-type modulated symbols detection. The Symbol Error Rate (SER) based strategy is proposed to design a low complexity Sparse AutoEncoder Detector (SAED) to tackle this problem. Furthermore, a Cumulant and Moment Feature Vector (CMFV)-based method is introduced for mixed symbols detection. Also, the designed symbol detector does not rely on a comprehensive knowledge of channel models and parameters but has the capability to detect various modulation signals. Simulation results show that the SER performance of the SAE symbol detector is close to the values of the Maximum Likelihood (ML) detection approach and provides a stable performance against phase offsets, frequency offsets, and under a limited training dataset.
- Wireless communications,
- Symbol Detection(SD),
- Deep Neural Network (DNN),
- Cumulant and moment feature vector,
- Frequency and phase offsets

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References(16)

References

[1]	PROAKIS J G. Digital Communications[M]. New York: McGraw-Hill, 2001: 112–180.
[2]	MENG Fan, CHEN Peng, WU Lenan, et al. Automatic modulation classification: A deep learning enabled approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10760–10772. doi: 10.1109/TVT.2018.2868698
[3]	郭晨, 简涛, 徐从安, 等. 基于深度多尺度一维卷积神经网络的雷达舰船目标识别[J]. 电子与信息学报, 2019, 41(6): 1302–1309. doi: 10.11999/JEIT180677 GUO Chen, JIAN Tao, XU Congan, et al. Radar HRRP target recognition based on deep multi-scale 1D convolutional neural network[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1302–1309. doi: 10.11999/JEIT180677
[4]	唐伦, 周钰, 谭颀, 等. 基于强化学习的5G网络切片虚拟网络功能迁移算法[J]. 电子与信息学报, 2020, 42(3): 669–677. doi: 10.11999/JEIT190290 TANG Lun, ZHOU Yu, TAN Qi, et al. Virtual network function migration algorithm based on reinforcement learning for 5G network slicing[J]. Journal of Electronics &Information Technology, 2020, 42(3): 669–677. doi: 10.11999/JEIT190290
[5]	FARSAD N and GOLDSMITH A. Neural network detection of data sequences in communication systems[J]. IEEE Transactions on Signal Processing, 2018, 66(21): 5663–5678. doi: 10.1109/TSP.2018.2868322
[6]	LIU Chang, WANG Jie, LIU Xuemeng, et al. Deep CM-CNN for spectrum sensing in cognitive radio[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(10): 2306–2321. doi: 10.1109/JSAC.2019.2933892
[7]	O’SHEA T and HOYDIS J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4): 563–575. doi: 10.1109/TCCN.2017.2758370
[8]	ZHANG Min, LIU Zongyan, LI Li, et al. Enhanced efficiency BPSK demodulator based on one-dimensional convolutional neural network[J]. IEEE Access, 2018, 6: 26939–26948. doi: 10.1109/ACCESS.2018.2834144
[9]	SAMUEL N, DISKIN T, and WIESEL A. Learning to detect[J]. IEEE Transactions on Signal Processing, 2019, 67(10): 2554–2564. doi: 10.1109/TSP.2019.2899805
[10]	GAO Xuanxuan, JIN Shi, WEN Chaokai, et al. ComNet: Combination of deep learning and expert knowledge in OFDM receivers[J]. IEEE Communications Letters, 2018, 22(12): 2627–2630. doi: 10.1109/LCOMM.2018.2877965
[11]	GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Cambridge: MIT Press, 2016: 306–319.
[12]	BISHOP C M. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006: 48–58.
[13]	SWAMI A and SADLER B M. Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on Communications, 2000, 48(3): 416–429. doi: 10.1109/26.837045
[14]	NIKIAS C L and MENDEL J M. Signal processing with higher-order spectra[J]. IEEE Signal Processing Magazine, 1993, 10(3): 10–37. doi: 10.1109/79.221324
[15]	YE Hao, LI G Y, and JUANG B H. Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2018, 7(1): 114–117. doi: 10.1109/LWC.2017.2757490
[16]	DÖRNER S, CAMMERER S, HOYDIS J, et al. Deep learning based communication over the air[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 132–143. doi: 10.1109/JSTSP.2017.2784180