“…The maximum likelihood hypothesis testing method based on decision theory theoretically ensures that its decision results are optimal under the Bayesian least-cost criterion and can guarantee the performance of the method in a certain low signal-to-noise environment. However, the main drawbacks of the method are as follows: more a priori knowledge is required, such as signal-to-noise ratio, carrier frequency, symbol rate, oversampling multiplier, and other parameters, and secondly, the existence of unknown parameters leads to a complex computational push-to process and high computational complexity, which is difficult to implement in practical production [5]. The classification function is relatively single, and all common modulation types cannot be identified by one framework; in the actual channel, the noise is not necessarily Gaussian white noise and there are multipath fading, time delay, Doppler effects, and other effects [6].…”