深度學習在物理層信號處理中的應用研究（物理層信號的功能特性）

關鍵詞：深度學習，信號檢測、MIMO

其中代表檢測閾值，取值范圍為。和代表檢測結果分別為正常和虛假上報。和分別為觀測信號在零假設和備擇假設下的后驗分布。根據[9]可得，假設檢驗的結果（誤報率和丟失率）與發送者的實際位置、上報位置、信道狀況和檢測閾值有關。對于接收端來說，發送者的實際位置、上報位置以及信道狀態屬于未知或部分已知的環境變量，在與發送者之間不斷的信息交互過程中，本文提出接收端可以基于DQN來不斷優化檢測閾值的選擇，從而提高信號檢測的準確率。

參考文獻

[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.

網絡智能體

版權聲明：本文內容由網絡用戶投稿，版權歸原作者所有，本站不擁有其著作權，亦不承擔相應法律責任。如果您發現本站中有涉嫌抄襲或描述失實的內容，請聯系我們jiasou666@gmail.com 處理，核實后本網站將在24小時內刪除侵權內容。