Artificial Intelligence in 6G Wireless Networks: Opportunities, Applications, and Challenges

International Journal of Intelligent Systems, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 27

Published: March 25, 2024

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With advancement in technology, profound techniques can potentially improve performance networks. Besides, artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, adaptation. A sophisticated AI will be required for next-generation networks automate information delivery between smart applications simultaneously. technologies, such as machines deep learning techniques, have attained tremendous success many recent years. Hances, researchers academia industry turned their attention advanced development AI-enabled This paper comprehensively surveys different various applications. Moreover, we present that exploit power enable desired evolution challenges unsolved research this area, which represent future trends networks, discussed detail. We provide several suggestions solutions help more handle complicated problems. In summary, deeply understand up-to-the-minute network designs based on identify interesting issues pursued a fast way.

Language: Английский

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Machine Learning Empowered Emerging Wireless Networks in 6G: Recent Advancements, Challenges and Future Trends

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 83017 - 83051

Published: Jan. 1, 2023

The upcoming 6G networks are sixth-sense next-generation communication with an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a connected, sustainable world. Recent developments in artificial intelligence (AI) have enabled wide range of novel technologies through the availability advanced machine learning (ML) models, large datasets, and high computational power. In addition, intelligent resource management is key feature that enables self-configuration self-healing by leveraging parallel computing autonomous decision-making ability ML techniques to enhance energy efficiency capacity networks. Consequently, will play significant role addressing mobility challenges wireless This article provides comprehensive review state-of-the-art algorithms applied networks, categorized into types, including supervised unsupervised learning, Deep Learning (DL), Reinforcement (RL), (DRL) Federated (FL). particular, we emerging paradigm, such as device-to-device (D2D) vehicular (Vnet), Fog-Radio Access Networks (F-RANs). We highlight ML-based solutions address technical terms allocation, task offloading, handover management. also provide detailed improve reduce latency To this end, identify open research issues future trends concerning applications

Language: Английский

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I-MEREC-T: Improved MEREC-TOPSIS scheme for optimal network selection in 5G heterogeneous network for IoT

Internet of Things, Journal Year: 2023, Volume and Issue: 22, P. 100748 - 100748

Published: March 11, 2023

Language: Английский

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Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis

Computer Networks, Journal Year: 2024, Volume and Issue: 245, P. 110358 - 110358

Published: March 30, 2024

Language: Английский

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Industry 5.0 in Smart Education: Concepts, Applications, Challenges, Opportunities, and Future Directions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 81938 - 81967

Published: Jan. 1, 2024

Industry 5.0 is one of the emerging stages industrialization in which humans collaborate with cutting-edge technologies to enhance various workplace processes. The primary objective emphasize meeting needs people and provide enhanced resilience understanding sustainability. enables cooperation such advanced stakeholders education sector ensure efficiency effectiveness teaching-learning process. present study provides an exhaustive review role smart education. At outset, a brief overview scenario its associated challenges presented. This sets stage for establishing need Education 1.0 progressive transition 4.0. Further, motivation integrate 4.0 related enabling that support are discussed. paper extensively description application educational sectors namely medical education, further learning, distance engineering shop floor training. also presents seven case studies highlighting successful implementation versatile regions. Finally, discussed pointing potential future directions research.

Language: Английский

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MADM-based network selection and handover management in heterogeneous network: A comprehensive comparative analysis

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101918 - 101918

Published: Feb. 21, 2024

As radio access technologies, processing speeds, and multimode interfaces of low-powered portable devices continue to advance, the future wireless communication is envisioned offer pervasive network coverage, high data rates, a wide spectrum services while maintaining mobility. High range services, huge connectivity, capacity, good geographic coverage are being provided by ultra-dense deployment small base stations (BSs) in heterogeneous networks (HWN). But dense BSs, mobility, heterogeneity, imbalanced traffic, dynamic user preferences lead frequent handover. Network overhead, excessive energy consumption, decrease service quality satisfaction can be due So, handover management one crucial challenges implementation 5G beyond HWNs for ensuring seamless efficiency, required experiences. The effectiveness decisions relies on suitable selection mechanism. Multi-attribute decision-making (MADM) used model analyze appropriate complexities considering broad intricate conflicting decision criteria efficient HWN. This article extensively explores, compares, analyzes vital MADM techniques utilized modeling strategies terms algorithmic strategies, cardinality, types significance attributes, utilities. also examines, analyzes, recognizes recent mobility trends utilizing tackle issues high-speed HWNs.

Language: Английский

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Maximizing throughput and energy efficiency in 6G based on phone user clustering enabled UAV assisted downlink hybrid multiple access HetNet

Telecommunication Systems, Journal Year: 2024, Volume and Issue: 85(4), P. 563 - 590

Published: Feb. 13, 2024

Language: Английский

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Toward Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 76606 - 76633

Published: Jan. 1, 2024

The next generation (6G) wireless communication technology has super advantages in high transmission rates scenarios. Internet of Things (IoT) been applied recent years due to its wide connection. However, effective resource optimization methods must be analyzed meet the requirements key performance indicators 6G IoT networks. This paper discusses a general investigation strategies system. study aims find main solutions optimize network performance. First, an overall summary current research is preferred latency, reliability, Energy Efficiency (EE), Spectrum (SE), bandwidth utilization efficiency, rate, and power efficiency. Second, we propose multi-indicator tradeoff associated with latest approaches investigate optimal strategies. Furthermore, show limitations discuss future works for devices communication. Our survey help researchers using advanced techniques.

Language: Английский

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A Comprehensive Survey on Resource Management in 6G Network Based on Internet of Things

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 113741 - 113784

Published: Jan. 1, 2024

Language: Английский

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AI-RAN in 6G Networks: State-of-the-Art and Challenges

IEEE Open Journal of the Communications Society, Journal Year: 2023, Volume and Issue: 5, P. 294 - 311

Published: Dec. 14, 2023

6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core network advanced into an intelligent network. The Artificial Intelligence Radio Access Network (AI-RAN) anticipated to offer features like reduced latency, improved bandwidth, data rates coverage. Furthermore, AI-RAN expected support complex use cases such as extreme connectivity, multi-user communications dynamic spectrum access. This paper provides detailed survey thorough assessment AI-RAN's vision state-of-the-art challenges. We first present concise introduction followed by background information current RAN its challenges must be overcome implement AI-RAN. then examines trending research issues in AI-RAN, i.e., related allocation, architecture, resource management. discuss methods these include adoption edge computing technologies boost performance conclude stating open directions.

Language: Английский

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