Interference identification model based on deep convolutional neural network and intelligent identification algorithm
A neural network and interference identification technology, applied in biological neural network models, neural learning methods, neural architectures, etc., to achieve the effects of clear physical meaning, reduced computational complexity, and improved models
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Embodiment 1
[0072] The first embodiment of the present invention is specifically described as follows. The system simulation adopts the python language and is based on the tensorflow deep learning framework, and the parameter setting does not affect the generality. This example verifies the validity of the proposed model and method, Figure 4 Verify the validity of the fixed-frequency interference mode. The parameters are set as: the frequency band of interference is 20MHz, the frequency resolution of spectrum sensing is 100kHz, the receiver performs full-band sensing every 1ms, and keeps the sensed spectrum data for 200ms. Therefore, S t The matrix size is 200×200, the interference signal bandwidth is 4MHz, the signal waveform is raised cosine wave, and the roll-off coefficient is α=0.5. The interference power is 30dBm. In Embodiment 1, two fixed-frequency interference modes are considered:
[0073] 1. Single-tone interference, the interference frequency is 2MHz.
[0074] 2. Multi-to...
Embodiment 2
[0077] The second embodiment of the present invention is specifically described as follows. The system simulation adopts the python language and is based on the tensorflow deep learning framework, and the parameter setting does not affect the generality. This example verifies the validity of the proposed model and method, Figure 4 To verify the validity of the fixed frequency interference mode, Figure 5 Verify the effectiveness of swept frequency interference identification. The parameters are set as: the frequency band of interference is 20MHz, the frequency resolution of spectrum sensing is 100kHz, the receiver performs full-band sensing every 1ms, and keeps the sensed spectrum data for 200ms. Therefore, S t The matrix size is 200×200, the interference signal bandwidth is 4MHz, the signal waveform is raised cosine wave, and the roll-off coefficient is α=0.5. The interference power is 30dBm. In Embodiment 2, the frequency sweep interference mode is considered: frequency ...
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