A taxonomy of AI techniques for 6G communication networks

Sheth, Karan; Patel, Keyur; Shah, Het; Tanwar, Sudeep; Gupta, Rajesh; Kumar, Neeraj

doi:10.1016/j.comcom.2020.07.035

Cited by 104 publications

(44 citation statements)

References 111 publications

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“…The growth of 6G systems will be enormous scope, multifaceted, dynamic, high complex, and heterogeneous. Moreover, 6G systems require helping consistent network and assurance different QoS necessities of different gadgets, just as cycle huge measure of information produced from actual conditions (Sheth et al, 2020). Manmade intelligence methods with robust assessment capacity, learning capacity, streamlining capacity and shrewd acknowledgment capacity, which can be utilized into 6G organizations to wisely do execution improvement, information disclosure, refined learning, structure association and convoluted dynamic (Chowdhury et al, 2020).…”

Section: Proposed Methodology Block Diagram and Flow Chartmentioning

confidence: 99%

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks

Mathew¹

2021

IJCSMC

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With the fast improvement of smart infrastructures and terminals, as well as an enhanced applications (such as augmented and virtual reality, holographic projection and remote surgery) with vivid prerequisites, modern networks (forthcoming 5G and 4G networks) will most likely be unable to satisfy the rapidly rising traffic needs. Likewise, efforts from both the scholarly and academia realm have effectively been put to the examination on 6G systems. Lately, man-made intelligence (AI) has been widely used as another worldview for the plan and enhancement of 6G systems with an undeniable degree of knowledge. Accordingly, the paper proposes AI-empowered architecture engineering for 6G systems to acknowledge information discovery, intelligent service provisioning, mechanic system adjustment, and smart resource management where the network architecture if segregated in four layers: smart application layer, intelligent control layer, information search and logic layer, and intelligent sensing layer. The article further survey and examine the uses of AI techniques for 6G organizations and expand how to utilize the AI procedures to efficiently and viably streamline the exaction of networks, including smart spectrum management, handover management, intelligent mobility, and AI-empowered mobile edge computing. The paper also highlights crucial future study directions and possible solutions for 6G networks such as energy management, hardware development, algorithms robustness, and computation efficiency.

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Section: Proposed Methodology Block Diagram and Flow Chartmentioning

confidence: 99%

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks

Mathew¹

2021

IJCSMC

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“…Not only that, the production of hardware and AI solutions are yet to achieve synchronism. In total, when 5G network is considered to use cloud driven AI, 6G network will use edge driven AI [80][81]. The Edge Intelligence (EI) is an extreme capability to process the data at the edge.…”

Section: E What Is the Scope Of Edge Intelligence In 6gmentioning

confidence: 99%

“…Consequently, it is necessary to re-define the real-time feedback by considering the online learning and pre-trained models. The training algorithms should be distributed instead of centralized and must adopt online training models by considering the edge device limitations [61,42,80].…”

Section: E What Is the Scope Of Edge Intelligence In 6gmentioning

confidence: 99%

6G Ecosystem: Current Status and Future Perspective

Bhat¹,

AlQahtani

2021

IEEE Access

139

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Next-generation of the cellular network will attempt to overcome the limitations of the current Fifth Generation (5G) networks and equip itself to address the challenges which become obvious in the future. Currently, academia and industry have focused their attention on the Sixth Generation (6G) network, which is anticipated to be the next big game-changer in the telecom industry. The outbreak of COVID'19 has made the whole world to opt for virtual meetings, live video interactions ranging from healthcare, business to education. However, we miss an immersive experience due to the lack of supporting technology. Experts have anticipated that starting from the post-pandemic age, the performance requirements of technology for virtual and real-time communication, the rise of several verticals such as industrial automation, robotics, and autonomous driving will increase tremendously, and will skyrocket during the next decade. In this manuscript, we study the latest perspectives and future megatrends that are most likely to drive 6G. Initially, we describe the instances that lead us to the vision of 6G. Later, we narrate some of the use cases and the KPIs essential to meet their performance requirement. Further, we highlight the key requirements of 6G based on contemporary research such as UN sustainability goals, business model, edge intelligence, digital divide, and the trends in machine learning for 6G.

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“…A novel method was designed in [8] for combining energy, computation, and communication (ECC). An AI-enabled 6G communication technology was developed in [9] for a broad range of future applications. A transmit diversity method was introduced in [10] to transmits messages over several paths to improve the signal-to-noise ratio.…”

Section: For Energy and Cost-efficient Communication A Multivariate Regressive Deep Stochasticmentioning

confidence: 99%

Bivalence Fuzzified Decision Stump Bootstrap Aggregation for Energy and Cost-Efficient 6G Communication

Rahim

Ramalingam

Ramachandran

et al. 2021

Preprint

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Future Sixth generation (6G) wireless networks are anticipatedto offer entirecoverage, improved spectral, energyandcost-efficient communication.The 6G will enable a network collectivelyand offer seamless wireless connectionsbetween the devices. While the deployment of 5G is ongoing, mobile communication networks are still suffering many basic challenges such as high-energy consumption and operating costs. To address these issues, it is very important to consider and develop new technologies in next-generation mobile communication, namely 6G. Novel machine learning can potentially assist the 6G to obtain better communication. Bivalence Fuzzified Decision Stump Bootstrap Aggregating (BFDSBA) model is introduced for energy and cost efficient communication. The BFDSBA model considers the nodes i.e. devices in the forecasting process before the data communication in the 6G network. The Bootstrap Aggregative technique utilizes set of weak learners as Bivalence Fuzzified Decision Stump. For each device in the network, energy, signal strength, and bandwidth is measured. Based on the estimated resources, efficient devices are selected for the 6G network architectural design. This in turn helps to improvedata communication with lesser cost in6G networks. The result exposesimprovement of BFDSBA model than the conventional methods.

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A taxonomy of AI techniques for 6G communication networks

Cited by 104 publications

References 111 publications

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks

6G Ecosystem: Current Status and Future Perspective

Bivalence Fuzzified Decision Stump Bootstrap Aggregation for Energy and Cost-Efficient 6G Communication

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A taxonomy of AI techniques for 6G communication networks

Cited by 104 publications

References 111 publications

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks﻿

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks﻿

6G Ecosystem: Current Status and Future Perspective

Bivalence Fuzzified Decision Stump Bootstrap Aggregation for Energy and Cost-Efficient 6G Communication

Contact Info

Product

Resources

About

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks

Artificial Intelligence and Cognitive Computing for 6G Communications & Networks