The invention relates to an
MIMO-SCFDE self-adaptive transmission scheme based on model-driven
deep learning. According to the method, a self-adaptive transmission model is established based on an
MIMO SCFDE
system. AMNet and ADNet are adopted to replace a
signal modulation part and a modulation identification part in a traditional
system respectively. The AMNet adopts a combined network taking a2D CNN, an LSTM and an FCDNN as sub-networks to form an integrated neural
network model, a modulation mode of a sending end is adjusted according to a channel condition of a receiving end, feature information extracted from a received
signal is input into the plurality of sub-networks, and conversion between features and an optimal modulation scheme are achieved according to network parameters obtained by training. Meanwhile, the receiving power under different path delays is selected as an adaptive factor to achieve
adaptive integration of each sub-network result. The ADNet completes adaptiveselection of a modulation
identification scheme based on the complexity of a
cyclic spectrum according to the
advantage that the
cyclic spectrum has accurate detection on the
signal type under a lowsignal-to-
noise ratio. The
system is more suitable for performance requirements of a 5G communication system.