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Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance. [ABSTRACT FROM AUTHOR]

[Display omitted] • A 3D soft-rigid PVDF@SnO 2 nanofiber layer is electrospun on copper current collector. • The soft nanofiber layers realize uniform Li+ flux and dendrite-free Li deposition. • Rigid SnO 2 offers abundant Li+ nucleation sites and promotes highly stable SEI layer. • Nanofiber flexible skeleton can accommodate and mitigate electrode volume expansion. • Such soft-rigid nanofiber layers account for prolonged cycle life of Li metal cells. Lithium (Li) metal is regarded as the most desirable anode candidates for high-energy–density batteries by virtue of its lowest redox potential and ultrahigh theoretical specific capacity. However, uncontrollable Li dendritic growth, infinite volume variation and unstable solid electrolyte interface (SEI) ineluctably plague its commercialization process. Herein, the three-dimensional (3D) nanofiber functional layers with synergistic soft-rigid feature, consisting of tin oxide (SnO 2)-anchored polyvinylidene fluoride (PVDF) nanofibers, are directly electrospun on copper current collector. This strategy can effectively regulate uniform Li deposition and strengthen SEI stability through the dual effect of physical accommodation and chemical ionic intervention. On the one hand, the nanofiber interlayers with excellent electrolyte affinity and well-distributed Li+ transport pathways can promote uniform Li+ flux distribution and large-size Li deposition. On the other hand, the rigid SnO 2 contributes to reducing Li nucleation overpotential and stabilizing SEI layer assisted by its spontaneous reaction with Li. As a result, the smooth and dense Li deposition is achieved by such soft-rigid nanofiber interlayers, thereby extending the cycling life and improving the safety application of Li metal batteries. This work offers a new route for efficient protection of Li metal anodes and brings a new inspiration for developing high-energy–density Li metal batteries. [ABSTRACT FROM AUTHOR]

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction. [ABSTRACT FROM AUTHOR]