Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN

Zhang, Chaofeng; Dong, Mianxiong; Ota, Kaoru

doi:10.1109/tccn.2020.2982886

Cited by 34 publications

(18 citation statements)

References 29 publications

(31 reference statements)

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“…The QoS of the C-RAN can be maximized by using DL for various purposes such as active RRH selection, bandwidth allocation, and traffic forecast. In [110], Zhang et al investigated a service-oriented maximum coverage problem to maximize communication coverage with minimum infrastructure. They also scrutinized a resource allocation decisionmaking problem to maximize the usage of bandwidth resources.…”

Section: Qos Maximizationmentioning

confidence: 99%

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A Survey on Applications of Deep Learning in Cloud Radio Access Network

Rodoshi

Choi

2021

IEEE Access

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The necessity for high-speed and low-latency connectivity of the vast number of mobile users is rising with the immense usage of mobile applications. A cloud radio access network (C-RAN) is a promising framework for next-generation cellular communication, which can satisfy the requirements of significantly increasing data traffic and user demands. In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations. Deep learning (DL) appears to be a feasible approach for facilitating the data processing capability, resource management in the cloud, and predicting dynamic traffic in cellular communication. The convergence of C-RAN and DL is expected to bring new prospects to both interdisciplinary research and industrial applications. In this regard, different approaches have been proposed for DL-based C-RAN in the literature. This article provides a comprehensive survey of the state-of-the-art DL techniques applied in C-RAN. A brief introduction of the C-RAN architecture and DL techniques is given to provide insights into these two emerging technologies. Existing surveys are also discussed to highlight the research gap. The reviewed works are categorized into power consumption optimization, network performance maximization, and QoS maximization based on their optimization objectives. The key ideas of DL applied in the reviewed schemes are also mentioned, and the performance evaluation techniques used in the research are discussed and compared. Lastly, research challenges and open research issues are highlighted to provide future research directions.INDEX TERMS Cloud radio access network, deep learning, evaluation techniques, optimization objectives, performance metrics.

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Section: Qos Maximizationmentioning

confidence: 99%

“…In [110], simulations were conducted for both the MSIO algorithm and the ARODQ algorithm. The performance of the MSIO was then compared with that of the conventional algorithm and the fast networking placement algorithm.…”

Section: Evaluation Techniques For Dl-based C-ranmentioning

confidence: 99%

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Rodoshi

Choi

2021

IEEE Access

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“…Then, the protocol analysis shared library unit finds the corresponding communication protocol template from the protocol type library according to the terminal device type, and finally the data analysis unit analyzes the original field data according to the communication protocol analysis template to obtain the type and content of the field data. Most of the local government investment and financing platforms currently do not have a sound early warning mechanism for financing risks, and it is difficult to achieve full process control of financing risks, and they can no longer predict risks in advance or detect them promptly, relying mainly on past financing experience to assess the scale and manner of future financing, combined with their profitability and 5 Journal of Sensors solvency for risk assessment [18]. These steps can only be part of the financing risk early warning work.…”

Section: Intelligent Government Control System Constructionmentioning

confidence: 99%

Intelligent Government Control System Construction Based on 5G IoT

2021

Journal of Sensors

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This paper presents an in-depth analysis of the construction of an intelligent government control system in the context of 5G IoT. The research has conducted performance tests and network tests on the samples of the components which debugged the problems that occurred during the tests and analyzed and improved the system. The system designed in this study is not only easy to maintain but also very reliable and safe, with a high level of accuracy, and can be used for remote “five remote” and management of switchgear and other related power equipment. Based on this model, a dual middleware PaaS service selection algorithm is proposed, and a utility evaluation strategy is used to load balance the PaaS service. The test results show that the DMPS algorithm has a high selection accuracy and load balancing capability. Combined with the experience of local government investment and financing platform financing risk management and control practices, the financing risk control system established through the study is made more feasible. From the development history of local government investment and financing platforms, we study local government investment and financing platforms from the perspective of historical development and explore the root causes of their high financing risks and possible defects in the management process of daily work. Concerning the existing laws and regulations, practical operation norms, the framework of the local government investment and financing platform system, and other objective conditions, we start to build a financing risk control system for local government investment and financing platforms from both practical and operational management aspects, to find a financing risk control system that is in line with the regulations and effective. Finally, the system will be put into practice, and the effectiveness of the system will be tested by using the simulation operation method, and the results will be adjusted in time for the shortcomings of the system. The system is expected to help local government investment and financing platforms to reduce their risks so that they can provide better services for urban infrastructure construction and become a solid link in the economic development chain.

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“…Deep learning implies an abstract layer analysis and hierarchical methods [8]. Additionally, deep learning is widely conducted in image processing [9], pattern recognition [10], 5G architecture [11], big data analysis [12], and Internet of Things [13]. Deep learning models are also the most widely adopted to solve nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Neural Network for the Residential Energy Consumption Prediction

Huang

Pang

Yang

et al. 2022

IEEJ Transactions Elec Engng

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In order to provide guidance for demand-side management and improve energy efficiency, the accuracy of residential electricity demand forecasting plays a significant role. Data-driven methods and deep learning network methods have been proved an effective methods for time series forecasting. In the context, the current research work proposes a novel neural network model based on convolutional neural network (CNN)-attention-bidirectional long-short term memory (BiLSTM) to predict residential energy consumption. The proposed approach combines CNN, attention mechanism, and BiLSTM, initially implementing CNN to extract the effective features of the original data. Subsequently, the extracted segments are employed as the input of attention-BiLSTM to predict energy consumption, where the attention mechanism comprehensively considers the output of BiLSTM neurons and assigns weights to neurons at each timestamp. In this article, we evaluate the proposed method using household electricity consumption data. This paper selects different input timestamp lengths (10/60/120 min, respectively) to validate the model's performance. We compare evaluation indicators (root mean square error, mean absolute error, mean absolute percentage error [MAPE]) between the proposed method and the state-of-the-art prediction methods. The experimental result shows that the proposed method achieves higher energy consumption forecasting accuracy and has the lowest average MAPE (3.7%).

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Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN

Cited by 34 publications

References 29 publications

A Survey on Applications of Deep Learning in Cloud Radio Access Network

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Intelligent Government Control System Construction Based on 5G IoT

A Deep Learning Neural Network for the Residential Energy Consumption Prediction

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