Resource Allocation for Ultradense Networks With Machine-Learning-Based Interference Graph Construction

Cao, Jiaqi; Peng, Tao; Liu, Xin; Dong, Weiguo; Duan, Ran; Yuan, Yifei; Wang, Wenbo; Cui, Shuguang

doi:10.1109/jiot.2019.2959232

Cited by 27 publications

(16 citation statements)

References 24 publications

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“…However, most studies that construct conflict graphs are based on accurate geographical distance information, which is not easy to collect in practice. Cao et al [119] then proposed an accurate and practical MLbased approach for constructing a conflict graph. Specifically, the inter-user interference relations are constructed by analyzing the data collected from the network with minimum prior knowledge assumed for training the ANN algorithm.…”

Section: A Throughput Maximizationmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Section: A Throughput Maximizationmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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“…Aspired by this fact, several contributions studied the use ML approaches in order to design suitable resource allocation policies. Indicative examples are (Ahmed and Khammari, 2018;Peng et al, 2019;Tauqir and Habib, 2019;Huang H. et al, 2020;Cao et al, 2020;Jang and Yang, 2020). In particular, in (Cao et al, 2020), a centralized NN was employed to return the channel allocation strategy that minimizes the co-channel interference in an ultra-dense wireless network.…”

Section: Mac and Rrm Layermentioning

confidence: 99%

“…Indicative examples are (Ahmed and Khammari, 2018;Peng et al, 2019;Tauqir and Habib, 2019;Huang H. et al, 2020;Cao et al, 2020;Jang and Yang, 2020). In particular, in (Cao et al, 2020), a centralized NN was employed to return the channel allocation strategy that minimizes the co-channel interference in an ultra-dense wireless network. The NN takes as input a binary matrix that contains the user-channel association and estimates the up-link SINR.…”

Section: Mac and Rrm Layermentioning

confidence: 99%

“…In addition, integrating AI in wireless networks is envisioned to bring a revolutionary transformation of conventional cognitive radio systems into intelligent platforms by unlocking the full potential of radio signals and exploiting new degrees-of-freedom (DoF) (Letaief et al, 2019;Saad et al, 2020). Identifying this opportunity, a significant amount of researchers turned their eyes on AI-empowered wireless systems and specifically in machine learning (ML) algorithms (see e.g., (Wang J. et al, 2020;Liu Y. et al, 2020;Jiang, 2020;Boulogeorgos et al, 2021a) and references therein). In more detail, in (Wang J. et al, 2020), the authors reviewed the 30year history of ML highlighting its application in heterogeneous networks, cognitive radios, device-to-device communications and Internet-of-Things (IoT).…”

Section: Introductionmentioning

confidence: 99%

“…In more detail, in (Wang J. et al, 2020), the authors reviewed the 30year history of ML highlighting its application in heterogeneous networks, cognitive radios, device-to-device communications and Internet-of-Things (IoT). Likewise, in (Liu Y. et al, 2020), big data analysis is combined with ML in order to predict the requirements of mobile users and enhance the performance of "social network-aware wireless." Moreover, in (Jiang, 2020), a brief survey was conducted that summarizes the basic physical layer (PHY) authentication schemes that employ ML in fifth generation (5G)-based IoT.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

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