Deep Q neural network anti-interference model and intelligent anti-interference algorithm

A neural network and model technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as the inability to expand the application environment of state decision space, and achieve the effect of reducing computational complexity

Active Publication Date: 2018-11-09
ARMY ENG UNIV OF PLA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it cannot be extended to application environments with large state decision spaces

Method used

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  • Deep Q neural network anti-interference model and intelligent anti-interference algorithm

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Embodiment 1

[0101] The first embodiment of the present invention is specifically described as follows. The system simulation adopts the python language, based on the caffe deep learning framework, and the parameter setting does not affect the generality. This example verifies the effectiveness of the proposed model and method, Figure 4 Validation of effectiveness against fixed jamming patterns. The parameters are set as follows: the interference and user frequency bands are 20MHz, and the frequency resolution of spectrum sensing is 100kHz. Users perform full-band sensing every 1ms and keep the perceived spectrum data for 200ms. Therefore, S t The matrix size is 200×200, the user signal bandwidth is 4MHz, and the user center frequency changes by 2MHz every 10ms, so K=9. Both the signal waveforms of the user and the interference are raised cosine waves, and the roll-off coefficient is α=0.5. The interference power is 30dBm, and the user's signal power is 0dBm. Demodulation Threshold β ...

Embodiment 2

[0105] The second embodiment of the present invention is specifically described as follows. The system simulation adopts the python language, based on the caffe deep learning framework, and the parameter setting does not affect the generality. This example verifies the effectiveness of the proposed model and method, Figure 4 Verify the effectiveness against fixed jamming patterns, Figure 5 Verify the effectiveness of countering dynamic jamming and intelligent jamming. The parameters are set as follows: the interference and user frequency bands are 20MHz, and the frequency resolution of spectrum sensing is 100kHz. Users perform full-band sensing every 1ms and keep the perceived spectrum data for 200ms. Therefore, S t The matrix size is 200×200, the user signal bandwidth is 4MHz, and the user center frequency changes by 2MHz every 10ms, so K=9. Both the signal waveforms of the user and the interference are raised cosine waves, and the roll-off coefficient is α=0.5. The inte...

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Abstract

The invention discloses a deep Q neural network anti-interference model and an intelligent anti-interference algorithm. A pair of emitting end and receiving end is a user, communication with the useris carried out, user communication is interfered by one or multiple jammers, the spectrum waterfall map of the receiving end is used as an input state of learning, and frequency domain and time domaincharacteristics of the interference are calculated. The algorithm comprises steps that firstly, a Q-value table corresponding to fitting is obtained through the deep Q neural network; secondly, a strategy is selected by the user according to the probability, training is carried out based on a return value of the strategy and the next environmental state, and the network weight and frequency selection strategy are updated; when the maximum number of cycles is reached, the algorithm ends. The model is advantaged in that the model is complete, the physical meaning is clear, the design algorithmis reasonable and effective, and the anti-interference scene based on the deep reinforcement learning algorithm can be excellently described.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, in particular to a deep Q neural network anti-jamming model and an intelligent anti-jamming algorithm. Background technique [0002] Due to the openness of the wireless communication environment, wireless communication systems are extremely vulnerable to malicious interference attacks. In addition, due to the rapid development of artificial intelligence technology, the level of interference intelligence is continuously improved, and future communication interference will present typical characteristics such as "waveform dexterity" and "decision intelligence", making traditional anti-jamming technologies (such as frequency hopping and spread spectrum) The anti-interference ability of the wireless communication system is obviously reduced, or even completely lost, which brings great challenges to the stability and security of the wireless communication system or network. Therefore, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/06H04W28/04G06N3/08G06N3/04
CPCH04W24/06H04W28/04G06N3/08G06N3/045
Inventor 王金龙徐煜华刘鑫徐逸凡李洋洋赵磊冯智斌
Owner ARMY ENG UNIV OF PLA
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