In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL. We then propose an NTL detection algorithm to solve linear system of equations (LSE) constructed by energy balance analysis with IMMs and the collector, and describe the hardware architecture of IMMs. Simulation results show that the proposed NTL detection algorithm successively detects the honesty of energy consumers as well as the energy loss from bypassing. Furthermore, it is observed the proposed detection framework is time-efficient and detection accuracy is at least 95% when variability of meter reading value is 80%.

In this letter, to improve data rate over wireless communication channels, we propose a deep Q network (DQN)-based adaptive modulation scheme by using Markov decision process (MDP) model. The proposed algorithm makes the reinforcement learning agent to select rate region boundaries as the states, which divide signal-to-noise ratio (SNR) range into rate regions. The simulation results show that spectral efficiency can be improved on the average by 0.5395 bps/Hz in wide SNR range.

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.

In this paper, we investigate a phase cancellation problem that occurs in tag-to-tag communication systems. These are the systems wherein several tags communicate with each other by backscattering an external constant wave signal. A transmitting tag modulates baseband information into the reflected signal using backscatter modulation. At the receiving tag, the received signal is the superposition of the transmitted signals by the carrier emitter and the transmitting tag. The resulting signal is demodulated using the envelope detector that causes the phase cancellation problem. This problem exists in all tag-to-tag communication systems and largely impacts on the reliability and communication range. We theoretically analyze and experimentally demonstrate this problem for the tags that use amplitude-shift keying-based backscattering. We propose an algorithm for the mitigation of the phase cancellation based on the phase rotation control. We examine the performance of the proposed algorithm through theoretical analysis and computer simulations. We confirm that the proposed algorithm can significantly mitigate the phase cancellation.