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To solve the problem of inaccurate recognition of types of communicationsignal modulation, a RNN neural network recognition algorithm combiningresidual block network with attention mechanism is proposed. In this method, 10kinds of communication signals with Gaussian white noise are generated fromstandard data sets, such as MASK, MPSK, MFSK, OFDM, 16QAM, AM and FM. Based onthe original RNN neural network, residual block network is added to solve theproblem of gradient disappearance caused by deep network layers. Attentionmechanism is added to the network to accelerate the gradient descent. In theexperiment, 16QAM, 2FSK and 4FSK are used as actual samples, IQ data frames ofsignals are used as input, and the RNN neural network combined with residualblock network and attention mechanism is trained. The final recognition resultsshow that the average recognition rate of real-time signals is over 93%. Thenetwork has high robustness and good use value.

A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms