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Unmanned aerial vehicles (UAV) can serve as aerial base stations for users due to their flexibility, low cost, and other characteristics. However, due to the high flight position of UAVs, the air-to-ground (ATG) channels usually dominate with line-of-sight (LoS), which can be easily eavesdropped by multiple eavesdroppers. This poses a challenge to secure communication between UAVs and ground users. In this paper, we study a UAV-aided secure communication in an urban scenario where a legitimate UAV Alice transmits confidential information to a legitimate user Bob on the ground in the presence of several eavesdroppers around it and a UAV Jammer sends artificial noise to interfere with the eavesdroppers. We aim to maximize the physical layer secrecy rates in the system by jointly optimizing the trajectories of UAVs and their transmitting power. Considering the time-varying characteristics of channels, this problem is modeled as a Markov decision process (MDP). An improved algorithm based on double-DQN is proposed in the paper to solve this MDP problem. Simulation results show that the proposed algorithm can converge quickly under different environments, and the UAV transmitter and UAV jammers can find the optimal location correctly to maximize the information secrecy rate. It also shows that the double-DQN (DDQN) based algorithm works better than the Q-learning and deep Q-learning network (DQN).

In this paper, we study a simultaneous wireless information and power transfer (SWIPT) system aided by the intelligent reflecting surface (IRS) technology, where an AP transmits confidential information to the legitimate information receiver (IR) in the presence of an energy harvesting (EH) receiver that could be a potential eavesdropper. We aim to maximize the secrecy rate at the legitimate IR by jointly optimizing the information beamforming vector and the energy transfer beamforming vector at the access point (AP), and the phase shift matrix at the IRS, subject to the minimum harvested power required by the EH receiver. The semi-definite relaxation (SDR) approach and the alternating optimization (AO) method are proposed to convert the original non-convex optimization problem to a series of semi-definite programs (SDPs), which are solved iteratively. Numerical results show that the achievable secrecy rate of the proposed IRS-assisted SWIPT system is higher than that of the SWIPT system without the assistance of the IRS.

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework, the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.