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In this paper, a detailed analysis of multi-input multi-output (MIMO) cross-layer secure communication algorithms in information-physical systems is investigated employing an interference strategy. A three-stage data-assisted channel estimation method is proposed in this paper for the acquisition of channel state information for complex jamming channels in large-scale MIMO two-layer systems. To implement the data-assisted scheme, assuming that there are no errors and no delay in the system, if the data detection and decoding data sequences are completed at a small cell base station, they are sent to the macro base station via a wired backhaul. Due to the sparsity of the channel at the macro base station after user grouping, a channel estimation algorithm based on optimal block orthogonal matching tracking(s) is proposed in the case where the downlink channel at the macro base station utilizes the decoded uplink data and known training sequences. The simulation results show that the data-assisted method proposed in this paper is effective in improving channel estimation accuracy. A machine learning algorithm is directly used to classify the channel difference or channel matrix to obtain the authentication results. In this paper, the scheme is first simulated using channel data from dynamic communication scenarios, its feasibility is analyzed, and the parameters in the scheme are compared, and the optimal scheme is the bagging tree authentication scheme using a 128-dimensional channel matrix as input. To address the interference problem caused by the dense arrangement of SAPs in heterogeneous networks and the unbalanced network load, large-scale MIMO techniques are introduced to reduce the downlink interference caused by the microcell boundary expansion in heterogeneous networks.

MIMO Cross-Layer Secure Communication Algorithm for Cyber Physical Systems Based on Interference Strategies