Application of Machine-Learning Based Prediction Techniques in Wireless Networks

Bhutani, Gitanjali

doi:10.4236/ijcns.2014.75015

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…A comprehensive review of the latest uses, applications, challenges, and methods of deep learning for UAVs was reported in [140]. Additionally, in the work done by [141], the different solved problems of wireless networks such as handover latency reduction, routing, link duration prediction, etc. were analyzed using machine learningbased prediction techniques, and further problems were also identified, to which these methods can be applied to them.…”

Section: Quad-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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This paper presents a review of the various control strategies that have been conducted to address and resolve several challenges for a particular category of unmanned aerial vehicles (UAVs), the emphasis of which is on the rotorcraft or rotary-wing systems. Initially, a brief overview of the important relevant definitions, configurations, components, advantages/disadvantages, and applications of the UAVs is first introduced in general, encompassing a wide spectrum of the flying machines. Subsequently, the focus is more on the two most common and versatile rotorcraft UAVs, namely, the twin-rotor and quadrotor systems. Starting with a brief background on the dual-rotor helicopter and a quadcopter, the full detailed mathematical dynamic model of each system is derived based on the Euler-Lagrange and Newton-Euler methods, considering a number of assumptions and considerations. Then, a state-of-the-art review of the diverse control strategies for controlling the rotorcraft systems with conceivable solutions when the systems are subjected to the different impediments is demonstrated. To counter some of these limitations and adverse operating/loading conditions in the UAVs, several innovative control techniques are particularly highlighted, and their performance are duly analyzed, discussed, and compared. The applied control techniques are deemed to produce a useful contribution to their successful implementation in the wake of varied constraints and demanding environments that result in a degree of robustness and efficacy. Some of the off-the-shelf developments in the rotorcraft systems for research and commercial applications are also presented.

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Section: Quad-rotor Systemsmentioning

confidence: 99%

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

2020

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“…In recent years, data-driven machine learning (ML) methods-including support vector machines, decision-tree learning, Bayesian networks, genetic algorithms, and rule-based learning-have been developed and utilized to support the design and operation of complex communication systems [12]. Very recently, innovative ML techniques, such as deep learning (DL) and reinforcement learning (RL) methods, have attracted attention for UAV-based wireless communications [13][14][15][16][17][18][19][20][21]. The advantage in using ML tools for the design of U-RANs stems from the fact that they easily allow one to take into account application-specific issues, such as, among the others, the choice of the best type of UAV [22][23][24][25][26][27][28], Doppler effects due to the UAV motion, dynamic positioning, interference management, and load balancing, which are instead difficult to incorporate into more conventional model-based design approaches.…”

Section: Introductionmentioning

confidence: 99%

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

et al. 2020

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A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

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Assessing Wireless Data Services with Machine Learning and Geostatistics

Mijangos

Bruns

2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Application of Machine-Learning Based Prediction Techniques in Wireless Networks

Cited by 4 publications

References 18 publications

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review

On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

Assessing Wireless Data Services with Machine Learning and Geostatistics

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