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A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions
Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life.
Need of 6G for the Metaverse Realization
The concept of the Metaverse aims to bring a fully-fledged extended reality environment to provide next generation applications and services.
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners.
Artificial Intelligence for the Metaverse: A Survey
Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform.
Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges
When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced.
Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data.
Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems
Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems.
UAV Communications for Sustainable Federated Learning
Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32. 95%, 63. 18%, and 78. 81% compared with the benchmarks.
Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning
As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal multiple-access.
Fusion of Federated Learning and Industrial Internet of Things: A Survey
In this paper, we provide a thorough overview on integrating FL with IIoT in terms of privacy, resource and data management.
Learning Constellation Map with Deep CNN for Accurate Modulation Recognition
Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems.
Chain-Net: Learning Deep Model for Modulation Classification Under Synthetic Channel Impairment
Modulation classification, an intermediate process between signal detection and demodulation in a physical layer, is now attracting more interest to the cognitive radio field, wherein the performance is powered by artificial intelligence algorithms.
Intelligent Radio Signal Processing: A Survey
Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics.
Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous success stories.
Deep Learning for Deepfakes Creation and Detection: A Survey
By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
