In a multi-cell MIMO system, the rate of edge users is limited by the inter-cell co-channel interference. The CoMP scheme which includes Joint Process (JP) and Coordinated Scheduling/Beamforming (CS/CB) was developed to reduce the inter-cell interference and enhance the edge rate. Because CS/CB can alleviate the overhead of network, it gains attention recently. In this paper, a modified zero forcing beamforming (ZFBF) is applied to downlink transmission in a two-cell MIMO system. In order to enhance system sum rate, a novel coordinated user scheduling algorithm is proposed. Firstly, we select users with high correlation among cross-channels as candidates, and then group users from candidates with high orthogonality among direct-channels, and match user groups in different cells as the final scheduling group pair. Simulations show that the proposed algorithm can achieve a higher system sum rate with low complexity than traditional scheduling algorithms.

The issue of a low minority class identification rate caused by data imbalance in anomaly detection tasks is addressed by the proposal of a GAN-SR-based intrusion detection model for industrial control systems. First, to correct the imbalance of minority classes in the dataset, a generative adversarial network (GAN) processes the dataset to reconstruct new minority class training samples accordingly. Second, high-dimensional feature extraction is completed using stacked asymmetric depth self-encoder to address the issues of low reconstruction error and lengthy training times. After that, a random forest (RF) decision tree is built, and intrusion detection is carried out using the features that SNDAE retrieved. According to experimental validation on the UNSW-NB15, SWaT and Gas Pipeline datasets, the GAN-SR model outperforms SNDAE-SVM and SNDAE-KNN in terms of detection performance and stability.