Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Li, Rongpeng; Zhao, Zhifeng; Zhou, Xuan; Ding, Guoru; Chen, Yan; Wang, Zhongyao; Zhang, Honggang

doi:10.1109/mwc.2017.1600304wc

Cited by 510 publications

(271 citation statements)

References 11 publications

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“…To realize an intelligent implementation of network slicing, artificial intelligence has attracted particular attentions. By enabling networks be capable of interacting with environments, a network can automatically recognize a new type of application, infer an appropriate provisioning mechanism and establish a required network slice [15]. Meanwhile, with network scenarios becoming heterogeneous and complicated, cost-efficient and low-complexity algorithms based on machine learning can be developed for practical implementations [16].…”

Section: A Related Workmentioning

confidence: 99%

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs' specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.Index Terms-fog radio access network, network slicing, reinforcement learning.

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Section: A Related Workmentioning

confidence: 99%

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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“…On the other hand, precise perception of network environment and timely decision-making in 5G rely on intelligent operation due to highly complex and time-varying network conditions [9]. With recent 3 success of Artificial Intelligence (AI) enabled technologies, network intelligentization becomes a nature choice.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Network Slicing for V2X Services Toward 5G

2019

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Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks has been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications.However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges.Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced. Index TermsV2X services, network slicing, artificial intelligence, deep reinforcement learning Jie Mei is with the Intelligent Computing and Communication (IC 2 ) Lab, Wireless Signal Processing and Network

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“…This learning method always requires prior training on a specific form of data for best prediction results. A comprehensive survey based on deep learning for wireless network and 5G is given by Zhang et al Similarly, Li et al use artificial intelligence in 5G networks for effective management of resources . In the cellular networks, predicting the interarrival time of packets can help in resolving many issues of wireless communication.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, deep learning demonstrates remarkable performance to solve the problems of wireless communication. 11,12 Deep learning neural networks can learn and predict the unknown values of data at a certain time instant, provided known data values for previous time periods. This learning method always requires prior training on a specific form of data for best prediction results.…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive survey based on deep learning for wireless network and 5G is given by Zhang et al 11 Similarly, Li et al use artificial intelligence in 5G networks for effective management of resources. 12 In the cellular networks, predicting the interarrival time of packets can help in resolving many issues of wireless communication. The interarrival time of packets can be considered as a time series, in which each upcoming packet time value has some relation with present and past time values.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep‐DRX: A framework for deep learning–based discontinuous reception in 5G wireless networks

Memon

Maheshwari

Shin

et al. 2019

Trans Emerging Tel Tech

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The tremendous advancement in various types of mobile devices with distinct services demands emerging 5G networks to deal with different kinds of traffic. The high data rates along with beam searching operation in 5G increase user equipment (UE) energy expenses. Discontinuous reception (DRX) is used in long‐term evolution network to save the power of UE. The DRX conserves the energy at the cost of increased latency. On the other hand, long short‐term memory (LSTM), a deep neural network, has shown incredible results in learning time‐varying sequences from data sets. In this article, we propose a novel idea to train LSTM network for prediction of next packet arrival time based on real wireless traffic trace (dataset). Then, we use the trained LSTM model to predict dynamic sleep time in DRX for 5G networks. Our proposed algorithm, deep learning–based DRX (Deep‐DRX) is able to make dynamic sleep cycle in 5G communications. Deep‐DRX achieves 10% and 30% of power saving with a mean delay of 1.006 ms and 1.05 ms for Trace 1 and Trace 2, respectively.

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Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Cited by 510 publications

References 11 publications

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Intelligent Network Slicing for V2X Services Toward 5G

Deep‐DRX: A framework for deep learning–based discontinuous reception in 5G wireless networks

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