In this paper, a two-layer game based joint time and power allocation method for an underlay Energy Harvesting Cognitive Radio (EH-CR) network is proposed. The method first models the interplay between the Primary User (PU) and the Secondary Users (SUs) as a Stackelberg game and then models the interplay among the SUs as a Supermodel game in the underlay EH-CR network. Later, a coefficient for evaluating fair ness is introduced in order to promote fairness among the SUs. Subsequently, the utility function of the primary network and the utility function of the secondary network are defined based on their individual profits. By maximizing the secondary network's utility function, the Supermodel game's Nash Equilibrium (NE) solution is achieved. Then, by substituting the NE solution of the Supermodel game into the utility function of the primary network and then maximizing the utility function of the primary network, the NE solution of the Stackelberg game is obtained. Finally, a deterministic strategy can be obtained, which is the time coefficient of equalized spectrum sensing and the equalized power allocation scheme instead of a probabilistic strategy. Simulation outcomes demonstrate that, under the condition of maintaining the communication quality of the PU, the PU's revenue when PH0 = 0.8 can be improved by 18.2% and when PH0 = 0.6 can be improved by 13.3% compared with the conventional method.

This article proposes a novel graph recurrent neural network (GRNN)-based approach for detecting the eavesdropping attacks in smart grid wireless communication systems enabled by simultaneous wireless information and power transfer (SWIPT). By leveraging the graph-centric nature of GRNNs, the proposed method effectively learns the topological structure and the edge features of the wireless sensor networks (WSNs), enabling the detection of the eavesdropping attacks in dynamic WSNs. This article mathematically models the channel state information (CSI) under the man-in-the-middle eavesdropping attacks based on the physical-layer security (PLS) in SWIPT networks. Moreover, this article sets up a real-world testbed to create the training and testing data sets. The proposed GRNN model can handle large-scale complex topologies and dynamic eavesdropping networks, accurately detect eavesdropping behaviors, and enhance the security of information transmission in WSNs. Simulation results demonstrate that, compared with the algorithms, such as support vector machine (SVM), K-nearest neighbors (KNNs), convolutional neural network (CNN), graph convolutional network (GCN), and gated recurrent unit (GRU), the proposed method exhibits stronger robustness under complex attack scenarios, achieving a detection accuracy of over 95%. This article provides a novel and effective graph learning solution for the smart grid wireless communication security, which is of great significance to ensure the stable and reliable operation of the smart grids.

Dual active bridge (DAB) converters are widely used in new energy electric vehicles. However, the traditional DAB converter has limited soft-switching range and high circulating currents under wide input voltage especially when both-side voltages are mismatched. As a result, large power loss and low efficiency are undesirably obtained. In this paper, a novel converter integrated Buck-Boost and DAB (IBBDAB) converter is proposed for superior performances under wide input voltage. The IBBDAB converter combines the advantages of the Four-Switch Buck-Boost (FSBB) converter and the DAB converter. Besides, partial switches of the two converters are shared to reduce conduction loss and the number of switches. Moreover, an improved modulation strategy based on dual-edge PWM and single-phase-shift (DE-PWM- SPS) is applied to the IBBDAB converter. Combined with the modulation strategy, zero voltage switching (ZVS) can be easily achieved for all switches of DAB converter. Simulation results are finally presented to verify the performances of the proposed converter.