Intelligent Allocation Strategy of Mobile Users for Multi-Access Edge Computing Resources

Unnisa, Nazeer; Tatineni, Madhavi

doi:10.1109/eti4.051663.2021.9619420

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Over the last decades, RIS is vastly preferred in wireless communication systems for optimizing security performance. It is also helpful in efficient beamforming of the THz 32 signals as it is complicated for realizing the digital beamforming at the THz band. In mmWave systems, 33 beam selection has been assumed as the essential factor, which is often carried out through deep learning methods.…”

Section: Anti‐jamming Thz Communication System: System Model and Chan...mentioning

confidence: 99%

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

Unnisa

Tatineni

Fatima

et al. 2023

Int J Communication

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Summary Wireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

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Section: Anti‐jamming Thz Communication System: System Model and Chan...mentioning

confidence: 99%

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

Unnisa

Tatineni

Fatima

et al. 2023

Int J Communication

Self Cite

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Machine Learning and Edge Computing for Industry 4.0 Applications: Concepts and Extensive Review

Barboni

2023

EAI/Springer Innovations in Communication and Computing

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Intelligent privacy preservation proctol in wireless MANET for IoT applications using hybrid crow search-harris hawks optimization

Pamarthi

Narmadha

2022

Wireless Netw

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Intelligent Allocation Strategy of Mobile Users for Multi-Access Edge Computing Resources

Cited by 4 publications

References 18 publications

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

Machine Learning and Edge Computing for Industry 4.0 Applications: Concepts and Extensive Review

Intelligent privacy preservation proctol in wireless MANET for IoT applications using hybrid crow search-harris hawks optimization

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