Recently, the topology control solutions that use static transmission power, transmission range, and link quality, might not be useful. The objective of this paper adapts the transmission power to be adjusted with external changes by applying a machine learning algorithms. We develop a traffic signature algorithm based on traffic clusters of the network sites that have the same behavior then we predict their upcoming changes and correspondingly. The contribution of this work is using this model to create an optimal power distribution function based on traffic load. Furthermore, we propose a

In free-space optical (FSO) communication, Distribution models such as lognormal, gamma-gamma, and k-distribution describe weak, moderate, and strong turbulence, respectively. Whereas Málaga (M) distribution is a powerful statistical model repeatedly mentioned in the literature due to its generality, Málaga (M) describes the three turbulence conditions while considering the pointing errors, represented by the jitter boresight displacement, of the communication beam. Exact and closed-form expressions for symbol error rate and outage probability are presented in this paper—furthermore, cases

Tracking Unmanned Aerial Vehicles (UAVs) is a significant surge in commercial or recreational use. The proposed tracking system is involved in determining the position and attitude angles of the UAVs in real-time. Data fusion of multiple sensors is one of the technologies used most in recent years and is based on real-time estimation of UAV's both position and attitude angles with high precision. In order to accomplish this objective, sensor fusion of an accelerometer, a gyroscope, a magnetometer, and a real-time kinematic global positioning system (RTK-GPS) sensors are implemented in this

In this paper, the performance of the free-space optical (FSO) system from ground-to-satellite is analyzed considering the combined effect of atmospheric turbulence and beam wandering employing M-ary phase-shift keying (MPSK). Key parameters of the vertical connection, such as satellite altitude, zenith angle, and beam size, are investigated. In order to improve the spectrum efficiency, an adaptive transmission approach is applied to ensure efficient channel capacity usage. The procedure depends on changing the modulation order of the MPSK scheme according to the instantaneous channel state

This study evaluates the performance of a secure ground-to-satellite free-space optical (FSO) system using a bipolar pulse amplitude modulation over modulated gamma fading channel. A closed-form expression is derived for the joint probability of a satellite-based continuous-variable quantum key distribution (CV-QKD) protocol that uses dual-threshold detection. Furthermore, to study the system behaviour, closed-form expressions for quantum bit-error-rate (QBER) and quantum bitdiscard rate (QBDR) are given. The accuracy of the proposed derivations is validated using Monte-Carlo simulations and

A virtualized radio access network (V-RAN) is considered one of the key research points in the development of 5G and the interception of machine learning algorithms in the Telecom industry. Recent technological advancements in Network Function Virtualization (NFV) and Software Defined Radio (SDR) are the main blocks towards V-RAN that have enabled the virtualization of dual-site processing instead of all BBU processing as in the traditional RAN. As a result, several types of research discussed the trade-off between power and bandwidth consumption in V-RAN. Processing at remote locations

In recent years, many probability models proposed to calculate the collision probability for each vehicle and those models used in collision avoidance algorithms and intersection management algorithms. In this paper, we introduce a method to calculate the collision probability of vehicles at an urban intersection. The proposed model uses the current position, speed, acceleration, and turning direction then each vehicle shares its required information to the roadside unit (RSU) via the Vehicle to Infrastructures (V2I). RSU can predict each vehicle's path in intersections by using the received

With the development of the 5G and Internet of things (IoT) applications, which lead to an enormous amount of data, the need for efficient data-driven algorithms has become crucial. Security concerns are therefore expected to be raised using state-of-the-art information technology (IT) as data may be vulnerable to remote attacks. As a result, this paper provides a high-level overview of machine-learning use-cases for data-driven, maintaining security, or easing telecommunications operating processes. It emphasizes the importance of analyzing the role of machine learning in the

The rising interest in assistive and autonomous driving systems throughout the past decade has led to an active research community in perception and scene interpretation problems like lane detection. Traditional lane detection methods rely on specialized, hand-tailored features which is slow and prone to scalability. Recent methods that rely on deep learning and trained on pixel-wise lane segmentation have achieved better results and are able to generalize to a broad range of road and weather conditions. However, practical algorithms must be computationally inexpensive due to limited resources

Filter bank multi-carrier (FBMC) is considered a promising alternative to the Orthogonal Frequency Division Multiplexing (OFDM) scheme. It improves spectral efficiency by eliminating the need for cyclic prefix while attenuating interference due to the robustness of the out-of-band emission. In this work, we present a framework, and the performance evaluation of FBMC is a multi-carrier modulation scheme for the direct detection of optical communications. As the proposed model has higher spectral efficiency than the classical ACO-OFDM, as removing the guard interval enhances the spectral