AI Inspired Intelligent Resource Management in Future Wireless Network

Fu, Sibao; Yang, Fan; Xiao, Ye

doi:10.1109/access.2020.2968554

Cited by 19 publications

(13 citation statements)

References 22 publications

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“…While AI is an unquestionable enabler, it is also very hard to find simple, trustable, explainable, and accurate models that perfectly satisfy the requirements of all the network operations and segments involved in ZTM. For this reason, big research efforts have been devoted to analyzing how the multi-disciplinary techniques of AI, ranging from ML to optimization theory and meta-heuristics, can cover the specific necessities of networking, for instance, in terms of granularity, timing, or data-awareness [119]- [121].…”

Section: Ai Techniques For Ztmmentioning

confidence: 99%

“…Nonetheless, for a more detailed description of ML taxonomies, we refer the reader to [23], [122]. Below, we provide an overview of how specific ML algorithms from all broad categories are suited to address specific needs in ZTM [119]- [121], [123], [124].…”

Section: Ai Techniques For Ztmmentioning

confidence: 99%

“…Moreover, this work studies the autonomous 5G traffic control from the perspective of SL, UL, and RL, showing the different stages in training and deployment, as well as the strengths and drawbacks of each method. In [121], AI is put as the central element of the intelligent wireless network architecture and the advantages and limitations of the most used AI techniques for intelligent resource management are summarized, including SL (e.g., NNs and SVM), UL (e.g., k-means and Principal Components Analysis (PCA)), RL, DL (e.g., Deep Neural Networks (DNNs)) and Deep Reinforcement Learning (DRL). In particular, the effectiveness of an RL algorithm called deep Q-learning for the joint resource allocation problem covering communication aspects, caching model, and computing model is studied.…”

Section: Ai Techniques For Ztmmentioning

confidence: 99%

See 2 more Smart Citations

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

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Mobile networks are facing an unprecedented demand for high-speed connectivity originating from novel mobile applications and services and, in general, from the adoption curve of mobile devices. However, coping with the service requirements imposed by current and future applications and services is very difficult since mobile networks are becoming progressively more heterogeneous and more complex. In this context, a promising approach is the adoption of novel network automation solutions and, in particular, of zero-touch management techniques. In this work, we refer to zero-touch management as a fully autonomous network management solution with human oversight. This survey sits at the crossroad between zero-touch management and mobile and wireless network research, effectively bridging a gap in terms of literature review between the two domains. In this paper, we first provide a taxonomy of network management solutions. We then discuss the relevant state-of-the-art on autonomous mobile networks. The concept of zero-touch management and the associated standardization efforts are then introduced. The survey continues with a review of the most important technological enablers for zero-touch management. The network automation solutions from the RAN to the core network, including end-toend aspects such as security, are then surveyed. Finally, we close this article with the current challenges and research directions.

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Section: Ai Techniques For Ztmmentioning

confidence: 99%

Section: Ai Techniques For Ztmmentioning

confidence: 99%

Section: Ai Techniques For Ztmmentioning

confidence: 99%

See 1 more Smart Citation

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado

Behravesh

Subramanya

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…Simulation is performed using MATLAB (i.e., version 2019b) [46] and Python (i.e., version 3.6) [47], which are the preferred tools for similar investigations in the literature [48,49]. MATLAB (or matrix laboratory) uses mathematical modeling to develop algorithms, compute large arrays and matrices, as well as accumulate and record statistics.…”

Section: Simulation Platformsmentioning

confidence: 99%

An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks

et al. 2022

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Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.

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“…Other than that, the variety of tasks in the network system is still too complicated to be managed. Therefore, AI and AI algorithms are used to manage and decide complex problems automatically [45]. From the beginning, AI was just a concept and in the imagination of screenwriters but, these days, these concepts and ideas have become true in society due to the development of advanced technology and it has an influence on the daily lives of people.…”

Section: Artificial Intelligencementioning

confidence: 99%

Blockchain, IoT and Fog Computing for Smart Education Management

Chaiyarak

Koednet

Nilsook

2020

International Journal of Education and Information Technologies

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The objective of this research is to present the use of blockchain technology, the internet of things and fog computing to develop the potential of school management for smart education. By using the blockchain to record and store various pieces of information the organization will create credibility of administrative data in all departments and will reduce corruption. Data can be examined transparently, especially the financial and budget management and procurement of durable articles. Regarding the academic work and registration, it can be used to store learners' information, educational history, information of enrollment, academic results, and participation in the various activities of the educational institutions. It can create credibility in the management of information about content, learning and ability assessment results that students receive, and these can be recorded and stored in a blockchain that can be safely distributed. In addition, IoT and fog computing are integrated with AI systems embedded in various modern devices to be applied in environmental management and smart education management. IoT and fog computing help to support and respond quickly to questions about the use of all types of equipment for real-time management of executives, personnel and students, as well as creating new work and learning styles that can solve the situation of dangerous communicable diseases, such as the COVID-19 situation, or can solve emergency situations that will emerge in the future by working and studying from home. It can organize and share resources for work and learning together to access the organization from outside anywhere and at any time

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AI Inspired Intelligent Resource Management in Future Wireless Network

Cited by 19 publications

References 22 publications

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks

Blockchain, IoT and Fog Computing for Smart Education Management

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