Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments

Obaidat

et al. 2020

Int J Communication

SummaryRouting remains the most important problem to be explored, for discovering an ideal pathway in unmanned aerial vehicles (UAVs). Several problems for identifying and targeting location continue giving the immense motility of UAVs. In routing planning, optimum decisions have to be taken for several vital operations for solving the issues in UAVs. Many types of routing techniques of UAVs, which have been used for many years, have been analyzed in this paper. Routing techniques aim to provide striking‐free surroundings to the UAVs and not only to find an optimum path but also the smallest path. Routing techniques play a vital part in the development of a secured way in a minimal time to reach the ultimate destination and hence much needed. There are various routing techniques, real‐time applications of UAVs which are elaborated in this paper, namely, representative, cooperative, and noncooperative techniques. In UAV, collision avoidance is very important for the obstacle‐free environment. Collision avoidance techniques are discussed for a safe environment of UAV. Further, the UAVs network communication coverage and connectivity by these techniques are discussed. Finally, we discuss the security and connectivity and future directions in UAV.

Section: Routing Techniquesmentioning

confidence: 99%

Routing and collision avoidance techniques for unmanned aerial vehicles: Analysis, optimal solutions, and future directions

Obaidat

et al. 2020

Int J Communication

“…Since our previous studies [13,14] deal with GP-based approaches only in numerical simulations, they need to be validated in the real-world settings. In this study, the performance of channel quality prediction using the GP in an urban environment was evaluated through several experiments.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Gaussian Process-Based Air-to-Ground Communication Quality Prediction in Urban Environments

Ładosz

Kim

et al. 2019

Sensors

Self Cite

This paper presents a detailed experimental assessment of Gaussian Process (GP) regression for air-to-ground communication channel prediction for relay missions in urban environment. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is introduced. Since water significantly absorbs wireless communication signal, water containers are utilized to replace buildings in a real-world city. To evaluate the performance of the GP-based channel prediction approach, several indoor experiments in an artificial urban environment were conducted. The performance of the GP-based and empirical model-based prediction methods for a relay mission was evaluated by measuring and comparing the communication signal strength at the optimal relay position obtained from each method. The GP-based prediction approach shows an advantage over the model-based one as it provides a reasonable performance without a need for a priori information of the environment (e.g., 3D map of the city and communication model parameters) in dynamic urban environments.

IEEE Robot. Autom. Lett.

“…Gradient following methods are unable to cope with non-linearities and discontinuities in the signal strength which are introduced by buildings. Meanwhile, learning-based approaches such as [10], [11] rely on collecting the signal strength data to update a priori communication model using machine learning techniques. Measurement-based approaches were used mainly for stationary environments thus far as the amount of data required for learning and prediction for mobile ground nodes was too high.…”

mentioning

confidence: 99%

A Hybrid Approach of Learning and Model-Based Channel Prediction for Communication Relay UAVs in Dynamic Urban Environments

Ładosz

Zheng

et al. 2019

Self Cite

This paper presents the trajectory planning of small UAVs for a communication relay mission in an urban environment. In particular, we focus on predicting the communication strength between air and ground nodes accurately to allow relay UAVs to maximise the communication performance improvement of networked nodes. In urban environments, this prediction is not easily achievable even with good mathematical models as each model is characterized by a series of parameters which are not trivial to obtain or estimate apriori and can vary during the mission. To address the difficulty, this work proposes to integrate a learning-based measurement technique with a probabilistic communication channel model. This hybrid approach is able to predict communication model parameters based on signal strength data which UAVs observe during the mission online, thus achieving better performance compared with the model-based approach in an urban environment. The predicted parameters are based on four discrete urban environment types. Numerical simulations validate the performance and benefit of the proposed approach. Index Terms-Aerial Systems: Applications, Learning and Adaptive Systems, Motion and Path Planning I. INTRODUCTION E STABLISHING wireless communication networks is vital in various circumstances such as rescue missions in a natural disaster. In urban environments, wireless communication is limited by the short coverage range and small bandwidth due to line-of-sight (LOS) obstruction by buildings. The following technologies have been used as an attempt to mitigate issues in urban environments: infrastructures (e.g. cell