Deep Learning for An Anti-Jamming CPM Receiver

被引：0
|
作者：
Qiu, Y. [1 ]
Brown, C. [1 ]
Li, L. [1 ]
机构：
[1] Govt Canada, Commun Res Ctr Canada Innovat Sci & Econ Dev, Ottawa, ON, Canada
来源：
MILCOM 2019 - 2019 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM) | 2019年
关键词：
Receiver; deep learning; continuous phase modulation; synchronization; frequency hopping; anti-jamming; acquisition; ACQUISITION; PHASE;
D O I：
10.1109/milcom47813.2019.9020788
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A novel continuous phase modulation (CPM) receiver model is proposed in this paper that employs deep learning (DL) techniques to improve the signal recovery and synchronization performance under heavy jamming conditions for frequency hopping (FH) based waveforms. A hybrid deep neural network is used to implement the DL in the receiver model. The simulation results show that the proposed receiver with DL boosting is robust under tone jamming which is a worst case scenario for a CPM receiver. The model achieves 3 - 5dB improvement under single-tone jamming, in terms of bit-error-rate (BER), and 2dB improvement under multi-tone jamming, compared with a receiver without DL.
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收藏
页数：6
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