Integrated Communication, Localization and Sensing towards 6G

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2491

Special Issue Editors

School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Interests: wireless localization; integration of sensing and communication; UWB; autonomous vehicle and intelligent transportation systems
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: key techniques for 5G and 6G systems; localization based on integration of satellite navigation and communication; network security based on data mining and machine learning; application of machine learning for wireless communication; design and development of cognitive radio systems
School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: wireless networks; cognitive radio; intelligent signal processing; biomedical electronics; deep space communications
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Interests: 5G mobile communications; integration of radar sensing and communication; convex/non-convex optimization millimetre-wave communication, antenna array signal processing, localization and radar signal processing and imaging

Special Issue Information

Dear Colleagues,

Investigations on 6G are now well underway and several large initiatives have been launched to define this new generation of wireless networks. Due to the wide variety of envisioned cases, 6G will not lead to a generalized one-fits-all solution, but rather generate a rich diversity in terms of devices, spectrum usage, and applications. In addition to communications, many emerging services in 6G are also based on localization and sensing. Accurate localization and tracking of active users, as well as passive objects, will be part of the core communications functionality, and will enable a wide variety of promising services, such as autonomous robotics, environment monitoring/surveillance, immersive augmented reality, etc.

Traditional communication and radio sensing (including radar sensing and wireless localization) systems are usually designed separately, and occupy different frequency bands. However, due to the wide deployment of millimeter wave and massive MIMO technologies, communication signals in future wireless systems will tend to have a high resolution in both the time and angular domains, thus enabling high-accuracy sensing using communication signals. As such, the integration of radio sensing, localization, and communications holds great potential in many spectrums and cost-limited scenarios by realizing dual/multiple functions besides communication, such as autonomous vehicle networking, Wi-Fi-based indoor localization, collaborative sensing, etc.

Research efforts are well underway to address the issues arising with communication, localization, and sensing, as well as the design of dual/multi-functional radar communications, to make full use of radio frequencies and hardware that are usually designed for only a single function. However, many challenges arise when wireless communication meets radio sensing and localization, such as unified theoretical frameworks, fundamental performance limits, waveform design, and signal processing in the integration of multi-functional systems, including localization, detection, imaging, and communication, as well as prototype implementations.

The goal of this Special Issue is to support a broad and diverse set of viewpoints from industry and academia on the development of specific technological enablers.

Prof. Dr. Tingting Zhang
Prof. Dr. Jiancun Fan
Prof. Dr. Qinyu Zhang
Dr. Pan Cao
Guest Editors

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Published Papers (2 papers)

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Research

16 pages, 20664 KiB  
Article
Joint Azimuth, Elevation and Delay Estimation for Single Base Station Localization in 3D IIoT
by and
Appl. Sci. 2023, 13(19), 10768; https://doi.org/10.3390/app131910768 - 27 Sep 2023
Viewed by 553
Abstract
Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we [...] Read more.
Integrated sensing and communication (ISAC) in the Industrial Internet of Things (IIoT) presents unique challenges in terms of localization techniques. While three-dimensional (3D) environments offer extra challenges to enhanced accuracy and realism, research in this area remains limited. To bridge this gap, we propose a novel localization technique assisted by a single base station (BS) in 3D IIoT scenarios. Our approach employs the MUltiple SIgnal Classification (MUSIC) algorithm to jointly estimate the angle of arrival (AoA) in azimuth and elevation, as well as the time of arrival (ToA). Compared to conventional multi-BS-assisted or MUSIC-based algorithms, our technique offers flexibility, easy implementation, and low computational cost. To improve performance, we integrate the Taylor-series into the iterative process after a MUSIC-based joint azimuth, elevation angle and delay estimation (JAEDE), resulting in a significant 99% reduction in computational complexity compared to a two-step MUSIC-based approach utilizing coarse-fine grid searching. Through numerical simulations, we compare our algorithm with three other MUSIC-based joint or separate estimation approaches, demonstrating its superior performance in azimuth angle of arrival (AoAz), elevation angle of arrival (AoAe), TOA, and overall location estimation across varying signal-to-noise ratio (SNR) conditions. Full article
(This article belongs to the Special Issue Integrated Communication, Localization and Sensing towards 6G)
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17 pages, 7610 KiB  
Article
An Improved Visual SLAM Method with Adaptive Feature Extraction
Appl. Sci. 2023, 13(18), 10038; https://doi.org/10.3390/app131810038 - 06 Sep 2023
Viewed by 1145
Abstract
The feature point method is the mainstream method to accomplish inter-frame estimation in visual Simultaneous Localization and Mapping (SLAM) methods, among which the Oriented FAST and Rotated BRIEF (ORB) feature-based method provides an equilibrium of accuracy as well as efficiency. However, the ORB [...] Read more.
The feature point method is the mainstream method to accomplish inter-frame estimation in visual Simultaneous Localization and Mapping (SLAM) methods, among which the Oriented FAST and Rotated BRIEF (ORB) feature-based method provides an equilibrium of accuracy as well as efficiency. However, the ORB algorithm is prone to clustering phenomena, and its unequal distribution of extracted feature points is not conducive to the subsequent camera tracking. To solve the above problems, this paper suggests an adaptive feature extraction algorithm that first constructs multiple-scale images using an adaptive Gaussian pyramid algorithm, calculates adaptive thresholds, and uses an adaptive meshing method for regional feature point detection to adapt to different scenes. The method uses Adaptive and Generic Accelerated Segment Test (AGAST) to speed up feature detection and the non-maximum suppression method to filter feature points. The feature points are then divided equally by a quadtree technique, and the orientation of those points is determined by an intensity centroid approach. Experiments were conducted on publicly available datasets, and the outcomes demonstrate the algorithm has good adaptivity and solves the problem of a large number of corner point clusters that may result from using manually set detection thresholds. The RMSE of the absolute trajectory error of SLAM applying this method on four sequences of TUM RGB-D datasets is decreased by 13.88% when compared with ORB-SLAM3. It is demonstrated that the algorithm provides high-quality feature points for subsequent image alignment, and the application to SLAM improves the reliability and accuracy of SLAM. Full article
(This article belongs to the Special Issue Integrated Communication, Localization and Sensing towards 6G)
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