In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

Over the last few years, drone base station technology has been recognized as a promising solution to the problem of network design for wireless communication systems, due to its highly flexible deployment and dynamic mobility features. This paper focuses on the 3D mobility control of the drone base station to boost transmission coverage and network connectivity. We propose a dynamic and scalable control strategy for drone mobility using deep reinforcement learning (DRL). The design goal is to maximize communication coverage and network connectivity for multiple real-time users over a time horizon. The proposed method functions according to the received signals of mobile users, without the information of user locations. It is divided into two hierarchical stages. Firstly, a time-series convolutional neural network (CNN)-based link quality estimation model is used to determine the link quality at each timeslot. Secondly, a deep Q-learning algorithm is applied to control the movement of the drone base station in hotspot areas to meet user requirements. Simulation results show that the proposed method achieves significant network performance in terms of both communication coverage and network throughput in a dynamic environment, compared with Q-learning algorithm.

The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively.

Indoor and outdoor positioning lets to offer universal location services in industry and academia. Wi-Fi and Global Positioning System (GPS) are the promising technologies for indoor and outdoor positioning, respectively. However, Wi-Fi-based positioning is less accurate due to the vigorous changes of environments and shadowing effects. GPS-based positioning is also characterized by much cost, highly susceptible to the physical layouts of equipment, power-hungry, and sensitive to occlusion. In this paper, we propose a hybrid of support vector machine (SVM) and deep neural network (DNN) to develop scalable and accurate positioning in Wi-Fi-based indoor and outdoor environments. In the positioning processes, we primarily construct real datasets from indoor and outdoor Wi-Fi-based environments. Secondly, we apply linear discriminate analysis (LDA) to construct a projected vector that uses to reduce features without affecting information contents. Thirdly, we construct a model for positioning through the integration of SVM and DNN. Fourthly, we use online datasets from unknown locations and check the missed radio signal strength (RSS) values using the feed-forward neural network (FFNN) algorithm to fill the missed values. Fifthly, we project the online data through an LDA-based projected vector. Finally, we test the positioning accuracies and scalabilities of a model created from a hybrid of SVM and DNN. The whole processes are implemented using Python 3.6 programming language in the TensorFlow framework. The proposed method provides accurate and scalable positioning services in different scenarios. The results also show that our proposed approach can provide scalable positioning, and 100% of the estimation accuracies are with errors less than 1 m and 1.9 m for indoor and outdoor positioning, respectively.

This paper verifies the feasibility and effectiveness of utilizing an integration technology of free-space optics (FSO) and satellite communications for an open radio access network (O-RAN) system while simultaneously transmitting fiber-optic sensing signals over the same FSO link. The achievement of O-RAN is to improve the flexibility, openness, and cost-effectiveness of current and next-generation wireless networks. Compared with radio wave-based communication, optical wireless communication (OWC), such as FSO, has many advantages, such as high-speed transmission, data confidentiality, and freedom from electromagnetic interference. Therefore, the use of FSO as a transmission link between ground radar transceivers for satellite communication and O-RAN systems will enhance the communication quality of satellite communication-based O-RAN systems. In particular, FSOs are small and easy to place at ground radar transceiver stations, satellite trucks, and communication target points. This makes it easier to quickly switch the transmission route of the radar transceiver station to different communication transmission and sensing monitoring target scenarios than physical optical fiber cable. In addition, in the fiber optic sensing part, use a machine learning model that combines advanced conditional generative adversarial network (CGAN) and convolutional neural network (CNN) models. This model aims to generate a large amount of sensor data and improve the sensing performance of fiber sensing systems. The experimental findings unambiguously show that the proposed integrated system achieves normal functioning in the communication part and outstanding temperature sensing measurement accuracy in the sensing part.