In this paper, a deep learning-based successive interference cancellation (SIC) scheme for use in nonorthogonal multiple access (NOMA) communication systems is investigated. NOMA has become a notable technique in the field of mobile wireless communication because of its capacity to overcome orthogonality, unlike a conventional orthogonal frequency division multiple access (OFDMA) communication system. In NOMA communication systems, SIC is one of the decoding schemes applied at receivers for downlink NOMA transmissions. In this paper, a convolutional neural network (CNN)-based SIC scheme is proposed to improve performance of the single base station and multiuser NOMA scheme. In contrast to existing SIC schemes, the proposed CNN-based SIC scheme can effectively mitigate losses resulting from imperfections of the SIC. The simulation results indicate that the CNN-based SIC method can successfully relieve conventional SIC impairments and achieve good detection performance. Consequently, a CNN-based SIC scheme can be considered as a potential technique for use in NOMA detection schemes.

One of the problems of the electricity grid system is electricity loss due to energy theft, which is known as non-technical loss (NTL). The sustainability and stability of the grid system are threatened by the unexpected electricity losses. Energy theft detection based on data analysis is one of the solutions to alleviate the drawbacks of NTL. The main problem of data-based NTL detection is that collected electricity usage dataset is imbalanced. In this paper, we approach the NTL detection problem using deep reinforcement learning (DRL) to solve the data imbalanced problem of NTL. The advantage of the proposed method is that the classification method is adopted to use the partial input features without pre-processing method for input feature selection. Moreover, extra pre-processing steps to balance the dataset are unnecessary to detect NTL compared to the conventional NTL detection algorithms. From the simulation results, the proposed method provides better performances compared to the conventional algorithms under various simulation environments.

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.