深度学习在物理层信号处理中的应用研究（物理层信号的功能特性）

关键词：深度学习，信号检测、MIMO

其中代表检测阈值，取值范围为。和代表检测结果分别为正常和虚假上报。和分别为观测信号在零假设和备择假设下的后验分布。根据[9]可得，假设检验的结果（误报率和丢失率）与发送者的实际位置、上报位置、信道状况和检测阈值有关。对于接收端来说，发送者的实际位置、上报位置以及信道状态属于未知或部分已知的环境变量，在与发送者之间不断的信息交互过程中，本文提出接收端可以基于DQN来不断优化检测阈值的选择，从而提高信号检测的准确率。

参考文献

[1] Mnih, Volodymyr, et al. "Human-levelcontrol through deep reinforcement learning." Nature 518.7540(2015): 529. https://www.nature.com/articles/nature14236.

[2] A. Mousavi and R. G. Baraniuk, “Learning toInvert: Signal Recovery via Deep Convolutional Networks,” Proc. IEEE Int’l.Conf. Acoustics Speech Signal Process. (ICASSP’17), New Orleans, LA, Mar. 2017,pp. 2272–76.

[3] C. Luo, J. Ji, Q. Wang, X. Chen and P. Li,"Channel State Information Prediction for 5G Wireless Communications: ADeep Learning Approach," in IEEE Transactions on Network Science andEngineering, early access.

[4] E. Nachmani, Y. Be’ery, and D. Burshtein,“Learning to decode linear codes using deep learning,” in Proc. Communication,Control, and Computing (Allerton), 2016, pp. 341–346.

[5] T. O’Shea and J. Hoydis, "An Introduction to Deep Learning for thePhysical Layer," in IEEE Transactions on Cognitive Communications andNetworking, vol. 3, no. 4, pp. 563-575, Dec. 2017.

[6] Y. He, C. Liang, F. R. Yu, N. Zhao, and H.Yin, “Optimization of cache-enabled opportunistic interference alignmentwireless networks: A big data deep reinforcement learning approach,” in Proc.IEEE Int. Conf. Commun. (ICC), May 2017, pp. 1–6.

[7] G. Han, L. Xiao, and H. V. Poor,“Two-dimensional anti-jamming communication based on deep reinforcementlearning,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP),New Orleans, USA, Mar. 2017, pp. 2087–2091.

[8] H. Ye, G. Y. Li, and B.-H. F. Juang, “Power ofDeep Learning for Channel Estimation and Signal Detection in OFDM Systems,”IEEE Wireless Commun. Lett., vol. 7, no. 1, Feb. 2018, pp. 114–17.

[9] Bai, Lin, Jinho Choi, and Quan Yu. “SignalProcessing at Receivers: Detection Theory.” Low Complexity MIMO Receivers,Springer, Cham, 2014. pp.5-28.

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