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Task-Oriented Communication and Sensing in Intelligent Wireless Network

Guest Editors:
Wei Wang: Tianjin Normal University, China
Qilian Liang: University of Texas at Arlington, USA
Xin Lu: Bournemouth University, UK 
Jinhwan Koh: GyeongSang National University, South Korea 
Xiantao Cai: Wuhan University, China

Submission Status: Closed   |   Submission Deadline: Closed

This collection is no longer accepting submissions


EURASIP Journal on Wireless Communications and Networking is calling for submissions to our Collection Task-Oriented Communication and Sensing in Intelligent Wireless Network.

With the rapid development of mobile communication technology, artificial intelligence technology and embedded technology, wireless network technology has been widely used in building, security, transportation, industry, agriculture, community and city management, geological disaster monitoring, energy, military and other related fields. Smart security, smart transportation, smart vehicle networking, smart agriculture, smart city, smart power grid, smart battlefield and other products and services have emerged. These intelligent wireless network systems are the key technologies to promote economic construction, emergency response, national defense and military development. These intelligent wireless network systems are the key technologies to promote economic construction, emergency response, national defense and military development.

The traditional communication pattern of transmitting original data first and then executing intelligent tasks is usually adopted in intelligent wireless network. That is, intelligent wireless network relies on sensing devices (such as cameras, etc.) to perceive and collect a large amount of data (such as text, pictures, etc.) and uses the traditional source/channel coding scheme to send the data to the edge/cloud servers after modulation. The edge/cloud servers demodulate the received signals and decode the channel/source to get the recovered original data. Then, artificial intelligence technologies represented by deep learning are used to understand and analyze the data (such as text, pictures, etc.) based on the recovered data, so as to complete a series of intelligent tasks, such as image classification and target recognition. In terms of sensing of intelligent wireless networks, network deployment is usually based on complete and accurate information acquisition, such as sensor network coverage, energy efficiency , and information connectivity between sensor nodes.

With the deep integration of sensing, communication and intelligent computing technologies, intelligent wireless network nodes have become intelligent terminals with strong communication and computing capabilities. The goal of communication and sensing in intelligent wireless network is no longer to accurately transmit bit data or accurately measure signal waveform, but to make the receiver to understand the information content of the sender or the surveyor. That is to take task-oriented as the starting point of communication and sensing in intelligent wireless network and realize a more refined "meaning" representation of the original data.

Given these challenges, this collection seeks to disseminate the latest research work in the domain of task-oriented communication and sensing algorithm and technologies in intelligent wireless network. It will solve the basic problems of communication and sensing in intelligent wireless network, and enhance the application value of intelligent wireless network in smart city, geological disaster monitoring, climate environment monitoring, intelligent military and other scenarios.

About the collection

The topics of interest include but are not limited to: 

• Task-Oriented Semantic Communications
• Task-Oriented Power Efficient Communications
• Task-Oriented Spectral Efficient in Wireless Network
• Task-Oriented Power Transfer in Wireless Network
• Task-Oriented Optimal Deployment of Sensor Network 
• Task-Oriented Drift Detection of Data Stream in Sensor Networks
• Task-Oriented Internet of Vehicle Communications
• Task-Oriented Communication and Sensing in Smart Cities
• Task-Oriented Communication and Sensing in Smart Grids

Image credit: © jamesteohart / Getty Images / iStock

  1. The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data...

    Authors: Hongting Zhai, Qingrui Zhang, Ruilin Tang, Yantong Zhang, Lili Sun, Qi Zhai, Ruochen Bian and Xinxin He
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:106
  2. In this paper, we concentrate on a non-orthogonal multiple access (NOMA)-enabled UAV data collection network for Internet of Things devices (IoTDs), where a unmanned aerial vehicle (UAV) is deployed as an aeri...

    Authors: Yu Du, Yijun Guo, Jianjun Hao and Hao Zhu
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:92
  3. Lip-reading is an emerging technology in recent years, and it can be applied to the field of language recovery, criminal investigation, identity authentication, etc. We aim to recognize what the speaker is say...

    Authors: Guangxin Xing, Lingkun Han, Yelong Zheng and Meirong Zhao
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:90
  4. When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal canc...

    Authors: Heping Shi, Guanghui Yan, Haijing Hou and Xiaoheng Jiang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:85
  5. As it is widely known, big data can comprehensively describe the inherent laws governing various phenomena. However, the effective and efficient analysis of available data has become a major challenge in the f...

    Authors: Hefei Gao, Yifei Yuan and Wei Wang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:81
  6. Deep learning (DL)-based beam training schemes have been exploited to improve spectral efficiency with fast optimal beam selection for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) sys...

    Authors: Haohui Jia, Na Chen, Taisei Urakami, Hui Gao and Minoru Okada
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:69
  7. In order to degrade the inter-user interference caused by the same beam selected for different users in mmWave massive MIMO systems, this paper proposes a joint beam selection combining cuckoo search (CS) and ...

    Authors: Chunhua Zhu, Qinwen Ji, Xinying Guo and Jiankang Zhang
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:65
  8. Pruning is showing huge potential for compressing and accelerating deep neural networks by eliminating redundant parameters. Along with more terminal chips integrated with AI accelerators for internet of thing...

    Authors: Chengchen Mao, Qilian Liang, Chenyun Pan and Ioannis Schizas
    Citation: EURASIP Journal on Wireless Communications and Networking 2023 2023:44

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Task-Oriented Communication and Sensing in Intelligent Wireless Network" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.