Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

Energy-efficient computation is important in mobile edge computing (MEC) systems. However, the computation efficiency problem in the unmanned aerial vehicle (UAV)-enabled MEC systems has been rarely researched. In this paper, a UAV-enabled MEC system under partial computation offloading mode is investigated. The computation efficiency is maximized by jointly optimizing the offloading times, the central processing unit frequencies, the transmit powers of the user and the trajectory of the UAV. For the non-convex computation efficiency problem, a two-stage iterative algorithm is proposed. Moreover, we derive the closed-form expressions for the local computation frequency and the transmit power of the user, which facilitates our algorithm implementation. Simulation results demonstrate that the computation efficiency of our proposed joint optimization scheme is better than those of other benchmark schemes.

In this paper, we investigate a novel simultaneous lightwave information and power transfer (SLIPT) in visible light communication (VLC) systems, where a photo diode (PD) and a solar panel are utilized as the information receiver and the energy harvester, respectively. By systematically analyzing both the information receiver and the energy harvester, we obtain the explicit expressions to characterize the illumination-rate-energy region. Based on the derived expressions, we investigate the downlink unicast transmission of multi-LED multi-user SLIPT VLC networks, and study the total transmit power minimization problem under the rate requirements, the minimum energy harvesting requirements, and dimming control constraints. To solve such non-convex problem, we exploit the semidefinite relaxation (SDR) technique and relax the problem into a convex problem, which can be efficiently solved via interior-point methods. Moreover, for the sake of users’ fairness, we further investigate the beamformer design to maximize the minimal rate under both minimum energy harvesting and dimming control constraints. Finally, the numerical results are provided to evaluate the proposed SLIPT system.

Visible light communication (VLC) and non-orthogonal multiple access (NOMA) are deemed two promising technologies in the next generation wireless communication systems in achieving high capacity and massive connectivity. In this paper we study the performance of a NOMA-enabled VLC system using different modulation schemes. In particular system level bit error rate (BER) is derived for different modulation schemes. Conventional methods used for analyzing the BER under orthogonal multiple access cannot be directly applied to NOMA. In order to obtain the closed-form BER expressions for the NOMA-enabled VLC systems, an analytical framework based on bitwise-decision axis and signal space is proposed. Moreover, the analysis method can be extended to the any wireless communication networks with NOMA.Simulation results demonstrate the accuracy of the theoretical analysis. The study shows that the BER gap among users decreases with the increase of the modulation order but at the cost of a higher power consumption in order to achieve a better signal to noise ratio. It is observed that 8-PSK modulation in NOMA-enabled VLC systems strikes a good tradeoff between the power cost and the achievable BER.

Wireless powered techniques have been recognized as promising techniques in future wireless communication systems, especially in cognitive radios (CRs) with energy-limit devices. However, most of the existing works focus on CRs with an ideal linear energy harvesting model. In this paper, a wireless powered wideband CR network is considered, and a practical non-linear energy harvesting model is adopted. To maximize the sum throughput of the secondary users, the energy harvesting time, channel allocation, and transmit power are jointly optimized. The closed-form expressions for the optimal transmit power and channel allocation are given. Simulation results show that there is a tradeoff between the harvesting energy and the sum throughput of the secondary users. It is also shown that the performance achieved under the non-linear energy harvesting model may equal to that achieved under the linear energy harvesting model.

Cognitive radio and non-orthogonal multiple access are promising for alleviating the severe spectral scarcity problem encountered by the next generation wireless communication systems. In this paper, in order to improve energy efficiency and spectral efficiency, a non-orthogonal multiple access cognitive radio network with simultaneous wireless information and power transfer is studied under a practical non-linear energy harvesting model. A multi-objective resource optimization problem is formulated for maximizing the harvesting power of each energy harvesting receiver. This problem is non-convex and challenging to solve. A weighted Tchebycheff method is applied to solve the formulated problem. It is shown that the performance achieved under the non-linear energy harvesting model is better than that obtained under the linear energy harvesting model.