ABSTRACT
The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method-either k-means or k-medoids-to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.
ABSTRACT
Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
ABSTRACT
The problem of independent control of the performance variables of a tower crane through a wireless sensor and actuator network is investigated. The complete nonlinear mathematical model of the tower crane is developed. Based on appropriate data driven norms an accurate linear approximant of the system, including an upper bound of the communication delays, is derived. Using this linear approximant, a dynamic measurable output multi delay controller for independent control of the performance outputs of the system is proposed. The controller performs satisfactory despite the nonlinearities of the model and the communication delays of the wireless network.
ABSTRACT
The present paper studies the prospective and the performance of a forthcoming high-speed third generation (3.5G) networking technology, called enhanced uplink, for delivering mobile health (m-health) applications. The performance of 3.5G networks is a critical factor for successful development of m-health services perceived by end users. In this paper, we propose a methodology for performance assessment based on the joint uplink transmission of voice, real-time video, biological data (such as electrocardiogram, vital signals, and heart sounds), and healthcare records file transfer. Various scenarios were concerned in terms of real-time, nonreal-time, and emergency applications in random locations, where no other system but 3.5G is available. The accomplishment of quality of service (QoS) was explored through a step-by-step improvement of enhanced uplink system's parameters, attributing the network system for the best performance in the context of the desired m-health services.
ABSTRACT
Two-way satellite broadband communication technologies, such as the Digital Video Broadcasting with Return Channel via Satellite (DVB-RCS) technology, endeavour to offer attractive wide-area broadband connectivity for telemedicine applications, taking into consideration the available data rates, Quality of Service (QoS) provision, survivability, flexibility and operational costs, even in remote areas and isolated regions where the terrestrial technologies suffer. This paper describes a wide-area tele-medicine platform, specially suited for homecare services, based on the DVB-RCS and Wi-Fi communication technologies. The presented platform combines medical data acquisition and transfer, patient remote monitoring and teleconference services. Possible operational scenarios concerning this platform and experimental results regarding tele-monitoring, videoconference and medical data transfer are also provided and discussed in the paper.
