Special Issues

Artificial Intelligence Techniques for Joint Sensing and Localization in Future Wireless Networks

Submission Deadline: 30 November 2021 (closed) View: 5

Guest Editors

Dr. N. Venkateswaran, Sri Sivasubramaniya Nadar College of Engineering (Autonomous), India.
Dr. Vijayakumar Ponnusamy, SRM Institute of Science and Technology, India.
Dr. Vinoth Babu Kumaravelu, Vellore Institute of Technology, India.
Dr. P.G. S Velmurugan, Thiagarajar College of Engineering, India.
Dr. Francisco, Autonomous University of San Luis Potosí, S.L.P, México.

Summary

Introduction – It is envisioned that the future wireless communication is more data driven. Given that new frequency bands with large bandwidth aided by mobile edge cloud, beamforming and AI techniques result in number of opportunities for joint sensing and localization. Fixed and mobile short range, adhoc, point to point communications can be realized in applications such as automotive, drones, robotics, health monitoring and many more ap. Such new applications can be realized by the combination sensing and localization and through the complex high dimensional data arising out of large number of observations.

Challenges – In wireless networks, it is always a challenge for high-accuracy cm-level user localization. Earlier, ML methods for localization mainly focused on some form of supervised learning strategy using regression and classification of data. ML techniques is not suitable when the data acquired is noisy, multi-modal with nonlinear characteristics Artificial Intelligence techniques have seen a massive rise in popularity due to its capability of end-to-end learning, prediction and decision making. AI methods can simultaneously model complex radio signal characteristics and fuse many inputs sensors. However, the challenge is in the form of lack of labelled data.

Aims: Through this special issue, experts and researchers in industry and academia are invited to submit their innovative ideas and practices through original research articles. The aim is to present a comprehensive collection on the challenges and research directions to facilitate combined sensing and localization towards a more reliable 6G mobile wireless networks.


Keywords

The special topics is not limited to:
• Propagation models
• Consistent channel models
• Passive and active sensing methods
• Sources and detectors
• Waveform designs and Channel estimation
• Chip Technologies increased output power and efficiency
• Energy-efficient techniques for accurate sensing and localization
• Real-time energy-efficient AI/ML techniques
• Online learning and adaptive models
• Optimization algorithms
• Computing resource optimization
• Intelligent reflective surfaces for enhanced mapping and localization
• Beam space processing for increased accuracy
• Cognitive-radio techniques
• Security and privacy algorithms for sensing and localization
• Sensing models for AoA and AoD estimation
• Metrics for evaluation

Published Papers


  • Open Access

    ARTICLE

    Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

    Tauqeer Safdar Malik, Kaleem Razzaq Malik, Muhammad Sanaullah, Mohd Hilmi Hasan, Norshakirah Aziz
    Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 809-824, 2022, DOI:10.32604/iasc.2022.021128
    (This article belongs to the Special Issue: Artificial Intelligence Techniques for Joint Sensing and Localization in Future Wireless Networks)
    Abstract Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU… More >

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