Generative Adversarial LSTM Networks Learning for Resource Allocation in UAV-Served M2M Communications

Xu, Yi‐Han; Liu, Xin; Zhou, Wen; Yu, Gang

doi:10.1109/lwc.2021.3075467

Cited by 24 publications

(12 citation statements)

References 10 publications

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“…In addition, additional works address resource allocation in UAV-related communications. Xu et al [31] proposed a long short-term memory with generative adversarial networks framework to solve the resource allocation problem that maximizes the sum rate of UAVs-served communications jointly, taking transmission power, transmission mode, frequency spectrum, relay selection, and the trajectory into account. The proposed framework has the benefit of tracking and predicting the mobility of UAVs.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Feng¹,

Chen²,

Huang³

et al. 2023

Computers, Materials &Amp; Continua

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Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power. A corresponding system model is summarized based on the scenario and existing theoretical works. The minimum throughputmaximizing object is then formulated as an optimization problem. As swarm intelligence algorithms are utilized effectively in numerous fields, this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms. This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem, as joint time and energy optimization have two sets of variables. The proposed method performs well in terms of stability and duration. Finally, performance is evaluated through numerical experiments. Simulation results demonstrate that the proposed method performs admirably in the given scenario.

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Section: Related Workmentioning

confidence: 99%

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Feng¹,

Chen²,

Huang³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…1) LSTM is a type of RNN where predictions are made based on long sequences of previous input values rather than on a single value [22]. LSTM-based techniques can improve significantly the learning speed, especially in problems with large state/action spaces.…”

Section: A Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Performance Analysis of ML-Based MTC Traffic Pattern Predictors

Ruiz-Guirola

López

Montejo‐Sánchez³

et al. 2023

IEEE Wireless Commun. Lett.

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Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-ofthe-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.

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“…The resource allocation issue for many UAVs serving M2M communications is investigated by Xu et al in [104]. Uncertainty in a stochastic setting is modeled after a Markov game.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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Generative Adversarial LSTM Networks Learning for Resource Allocation in UAV-Served M2M Communications

Cited by 24 publications

References 10 publications

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

Performance Analysis of ML-Based MTC Traffic Pattern Predictors

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

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