UAV Spectrum Sensing of Normalized Spectrum based on GRU network

Luo, Zhiyong; Xiong, Jiu; Luo, Feifan; Wang, Xiti; Shan, Chun; Jin, Shichao; Liu, Dunge

doi:10.1109/icus52573.2021.9641069

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…This is due to the UAV environment's continual change. Luo et al in [111] integrated machine learning and data preprocessing to enhance UAV spectrum sensing performance. The satisfactory simulation proves the superiority of their GRU-based approach over LSTM networks.…”

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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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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“…GRUs have been shown to be able to achieve comparable performance as LSTM in several applications, such as speech recognition, traffic load prediction, etc., with lower computational complexity [49][50][51].…”

Section: Gated Recurrent Unit Networkmentioning

confidence: 99%

Complexity Reduction over Bi-RNN-Based Nonlinearity Mitigation in Dual-Pol Fiber-Optic Communications via a CRNN-Based Approach

Shahkarami¹,

Yousefi²,

Jaouën³

2022

Preprint

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Bidirectional recurrent neural networks (bi-RNNs), in particular bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models, have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a unidirectional many-to-one vanilla RNN operate in tandem, each best capturing one set of channel impairments while compensating for the shortcomings of the other. We deploy this model in two different receiver configurations. In one, the neural network is placed after a linear equalization chain and is merely responsible for nonlinearity mitigation; in the other, the neural network is directly placed after the chromatic dispersion compensation and is responsible for joint nonlinearity and polarization mode dispersion compensation. For a 16-QAM 64 GBd dual-polarization optical transmission over 14 × 80 km standard single-mode fiber, we demonstrate that the proposed hybrid model achieves the bit error probability of the state-of-the-art bi-RNN-based methods with greater than 50% lower complexity, in both receiver configurations.

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