Deep Learning Approach for Predicting Energy Consumption of Drones Based on MEC

Abdellah, Ali R.; Alzaghir, Abbas; Koucheryav, Andrey

doi:10.1007/978-3-030-97777-1_24

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…The proposed strategy can improve the performance of UAV communication systems by outperforming conventional methods in terms of energy efficiency and communication fairness [35] Deep Learning…”

Section: Papermentioning

confidence: 99%

“…A deep learning approach to predicting drone energy consumption is discussed in [35] The authors model the drones' energy consumption based on flight time, altitude, speed, payload, and weather conditions using a deep neural network. The findings demonstrate that the proposed method is applicable to real-world scenarios and can accurately predict drones' energy consumption.…”

Section: Papermentioning

confidence: 99%

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A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Haque¹,

Chowdhury²,

Hassanalian³

2023

Preprint

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The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.

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Section: Papermentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology

Haque¹,

Chowdhury²,

Hassanalian³

2023

Preprint

View full text Add to dashboard Cite

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“…It also shows how difficult it is for RNNs to learn and maintain longterm memory and that they are limited in their ability to influence data. Long-short-term memories (LSTMs) were suggested to overcome the vanishing gradient issue in [4,[14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

et al. 2023

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This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

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