A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms
This work addresses modulation recognition for communication systems, but it appears incremental as it builds on existing RNN methods with standard enhancements.
The paper tackled the problem of inaccurate recognition of communication signal modulation types by proposing a neural network algorithm combining residual networks and attention mechanisms, achieving an average recognition rate of over 93% on real-time signals.
To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed. In this method, 10 kinds of communication signals with Gaussian white noise are generated from standard data sets, such as MASK, MPSK, MFSK, OFDM, 16QAM, AM and FM. Based on the original RNN neural network, residual block network is added to solve the problem of gradient disappearance caused by deep network layers. Attention mechanism is added to the network to accelerate the gradient descent. In the experiment, 16QAM, 2FSK and 4FSK are used as actual samples, IQ data frames of signals are used as input, and the RNN neural network combined with residual block network and attention mechanism is trained. The final recognition results show that the average recognition rate of real-time signals is over 93%. The network has high robustness and good use value.