High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

Ma, Zheng; Xiao, Ming; Xiao, Yao; Pang, Zhibo; Poor, H. Vincent; Vucetic, Branka

doi:10.1109/jiot.2019.2907245

Cited by 209 publications

(111 citation statements)

References 143 publications

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“…A list of emerging technology initiatives of incorporating AI schemes for communication research is provided by IEEE Communications Society. 1 This section selects and introduces the latest research progress of deep learningdriven wireless communication from the aspects of endto-end communication, signal detection, channel estimation, channel estimation and compression sensing, encoding and decoding, and security and privacy.…”

Section: Emerging Deep Learning Technologies In Wireless Communicationsmentioning

confidence: 99%

“…Along with the incredible growth of mobile data generated in internet of things (IoT) and the explosion of complicated wireless applications, e.g., virtual reality (VR) and augmented reality (AR), the fifth-generation (5G) technology demonstrates high-dimensional, highcapacity and high-density characteristics [1,2]. Moreover, future wireless communication systems will become ever-more demanding for edge-cloud computing since the edge servers are in proximity of the IoT devices and communicate with them via different wireless communication technologies [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Deng

2020

J Cloud Comp

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Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [7,8], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications. Deep learning (DL) is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with tremendous complex calculations, and is regarded as one of the effective tools for dealing with communication issues. Although deep learning has performed well in some IoT applications, "no free lunch" theorem [9] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a wide range of communication scenarios. This means that for any particular mobile and wireless network issue, we still need to adopt different deep learning architectures such as convolutional neural networks (CNN), deep neural networks (DNN) and recurrent neural networks (RNN), in order to achieve better performance of the communication systems.As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system ...

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Section: Emerging Deep Learning Technologies In Wireless Communicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Deng

2020

J Cloud Comp

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“…(1) Brief introduction Power transformer is one of the key equipment of power system, and its reliable operation characteristic is the guarantee of the whole power mechanism to supply power safely. The fault of power transformer will directly interfere with the normal operation of the power system, and also cause serious harm to the economy of power companies and power users [11]. At present, the power system is developing rapidly in the direction of ultra-high voltage, large power grid and automation, and the influence and interference of transformer fault on the normal operation of power system is becoming more and more serious [12].…”

Section: Power Transformermentioning

confidence: 99%

Power Transformer Fault Diagnosis System Based on Internet of Things

Wang

Liu

Chen

et al. 2020

Preprint

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Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective: To explore the utility of Internet of things in power transformer fault diagnosis system. Methods: A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results: The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.000 l, and it can correctly identify the fault types of the non-training samples. It can be seen that the technical support of the Internet of things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

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“…One short data packet of MTC devices is made up of information data and metadata, where the latter may be from media-accesscontrol layer and higher layers. Compared to the information data size, the size of metadata, containing various information such as logical addresses, packet initiation and termination, synchronization and security, may not be negligible for MTC devices [4].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Short Packet Communications for Uplink NOMA-Based Massive MTC Networks

Han

Liu

et al. 2019

IEEE Trans. Veh. Technol.

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The 5th generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%.

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High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

Cited by 209 publications

References 143 publications

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Power Transformer Fault Diagnosis System Based on Internet of Things

Energy-Efficient Short Packet Communications for Uplink NOMA-Based Massive MTC Networks

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