Artificial Intelligence Defined 5G Radio Access Networks

Yao, Miao; Sohul, Munawwar M.; Marojevic, Vuk; Reed, Jeffrey H.

doi:10.1109/mcom.2019.1800629

Cited by 107 publications

(48 citation statements)

References 9 publications

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“…where T RTT Proa,K,0 is given in (6) 6 , µ implies the maximum number of the HARQ round trips under the latency constraint (for the Proactive scheme under the latency constraint T K Proa + 4, µ = 0), ν implies the updated TTI index for the latent access failure probability in each HARQ round trip.…”

Section: Proactive Schemementioning

confidence: 99%

“…The Fifth Generation (5G) New Radio (NR) considers three new communication service categories: enhanced Mobile Broadband (eMBB), massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communications (URLLC) [1] [2]. Among them, the URLLC service is an essential element for applications, including factory automation [3], automation vehicles [4], remote control [5], and virtual/augmented reality (VR/AR) [6], which has stringent requirements on low latency and high reliability for small packets transmissions. The Third Generation Partnership Project (3GPP) has defined a general URLLC requirement: 1 − 10 −5 reliability within 1ms user plane latency 1 for 32 bytes (0.5ms for both downlink (DL) and uplink (UL)) [1].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Grant-Free Access for URLLC Service

Yan

Deng

Elkashlan

et al. 2021

IEEE J. Select. Areas Commun.

112

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5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grantbased (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper. Specifically, we present a spatiotemporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

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Section: Proactive Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing Grant-Free Access for URLLC Service

Yan

Deng

Elkashlan

et al. 2021

IEEE J. Select. Areas Commun.

112

View full text Add to dashboard Cite

show abstract

“…In view of this difficulty and uncertainty, CNN algorithm has been widely used in the detection of cancer CT images and the prediction of cancer recurrence after surgery due to its strong recognition ability and high prediction accuracy. American artificial intelligence AI makes use of deep science (CNN) to diagnose and treat cancer [20]. By letting artificial intelligence algorithm learn CT images of cancer that far exceed the lifetime number of visits by human doctors, it trains a deep convolutional neural network model to detect the canceration of normal cells, so as to achieve the purpose of early detection and treatment.…”

Section: B Application Of Cnn In Medicinementioning

confidence: 99%

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Han

et al. 2020

IEEE Access

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“…Traffic flow prediction has been considered as a key functional component of intelligent transportation systems. Meanwhile, artificial intelligence technology is rapidly growing and the fifth-generation communication technology is approaching [7][8][9][10][11][12][13][14][15]. Massive traffic data are being continuously collected through all kinds of sources, some of which can be treated and utilized as streaming data for understanding and predicting urban traffic [6].…”

Section: Related Workmentioning

confidence: 99%

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

Lin

Gao

et al. 2020

Wireless Communications and Mobile Computing

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Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

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Artificial Intelligence Defined 5G Radio Access Networks

Cited by 107 publications

References 9 publications

Analyzing Grant-Free Access for URLLC Service

Analyzing Grant-Free Access for URLLC Service

Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

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