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Signal Processing for Wireless Sensor Networks and Communication System
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Signal Processing for Wireless Sensor Networks and Communication System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 4526

Special Issue Editors

Dr. Andrew Zhang

E-Mail Website
Guest Editor
The Global Big Data Technologies Centre, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: signal processing for wireless communication and sensing; joint communication and sensing; radio sensing and pattern analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Yang

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Interests: mobile communications; satellite communications
Dr. Zhitong Ni

E-Mail
Guest Editor
The Global Big Data Technologies Centre, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: signal processing for wireless communication and sensing; joint communication and sensing; millimetre wave communications and sensing

Special Issue Information

Dear Colleagues,

There is an increasing demand for advanced signal processing techniques for sensing and communications. Advanced machine-type communication techniques are needed to support massive wireless sensors with various delay and data rate requirements. Wireless sensors with more intelligence are also in demand. Integrated sensing and communication techniques, which integrate the two functions in one system, are also emerging as an attractive solution, achieving significant savings in terms of power consumption and device size and cost. We shall see an unprecedented revolution in our physical world for wireless devices and applications.

This Special Issue seeks innovative digital signal processing solutions to important sensing and communication problems. The scope of this Special Issue includes (but is not limited to):

  • Integrated sensing and communications;
  • Millimetre wave radar signal processing techniques;
  • RFID sensing and localization;
  • Machine learning and pattern analysis techniques for wireless sensors;
  • Signal processing for machine-type communications.

Dr. Andrew Zhang
Prof. Dr. Kai Yang
Dr. Zhitong Ni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integrated sensing and communications (ISAC)
  • millimeter-wave radar
  • machine-type communications
  • signal processing
  • machine learning

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Published Papers (3 papers)

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Research

11 pages, 484 KiB  
Article
Doubly Constrained Waveform Optimization for Integrated Sensing and Communications
by Zhitong Ni, Andrew Jian Zhang, Ren-Ping Liu and Kai Yang
Sensors 2023, 23(13), 5988; https://doi.org/10.3390/s23135988 - 28 Jun 2023
Cited by 1 | Viewed by 1096
Abstract
This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. [...] Read more.
This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are developed to solve the original problems, which are NP-hard and cannot be directly solved by conventional semi-definite programming (SDP) techniques. Novel gradient descent methods are developed to solve the first two problems. For the third non-convex optimization problem, we transform it into a convex problem and solve it via convex toolboxes. We also disclose the connections between three optimizations using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions. Full article
(This article belongs to the Special Issue Signal Processing for Wireless Sensor Networks and Communication System)
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14 pages, 6388 KiB  
Article
Micro-Motion Extraction for Marine Targets by Multi-Pulse Delay Conjugate Multiplication and Layered Tracking
by Tong Mao, Yi Zhang, Kaiqiang Zhu and Houjun Sun
Sensors 2023, 23(8), 3837; https://doi.org/10.3390/s23083837 - 9 Apr 2023
Cited by 1 | Viewed by 1504
Abstract
The detection and recognition of marine targets can be improved by utilizing the micro-motion induced by ocean waves. However, distinguishing and tracking overlapping targets is challenging when multiple extended targets overlap in the range dimension of the radar echo. In this paper, we [...] Read more.
The detection and recognition of marine targets can be improved by utilizing the micro-motion induced by ocean waves. However, distinguishing and tracking overlapping targets is challenging when multiple extended targets overlap in the range dimension of the radar echo. In this paper, we propose a multi-pulse delay conjugate multiplication and layered tracking (MDCM-LT) algorithm for micro-motion trajectory tracking. The MDCM method is first applied to obtain the conjugate phase from the radar echo, which enables high-precision micro-motion extraction and overlapping state identification of extended targets. Then, the LT algorithm is proposed to track the sparse scattering points belonging to different extended targets. In our simulation, the root mean square errors of the distance and velocity trajectories were better than 0.277 m and 0.016 m/s, respectively. Our results demonstrate that the proposed method has the potential to improve the precision and reliability of marine target detection through radar. Full article
(This article belongs to the Special Issue Signal Processing for Wireless Sensor Networks and Communication System)
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10 pages, 2034 KiB  
Communication
Efficient Space–Time Signal Processing Scheme of Frequency Synchronization and Positioning for Sensor Networks
by Yung-Yi Wang and Jian-Rung Huang
Sensors 2023, 23(4), 2115; https://doi.org/10.3390/s23042115 - 13 Feb 2023
Cited by 2 | Viewed by 1179
Abstract
The orthogonal frequency division multiple access (OFDMA) technique has been widely employed in sensor networks as the data modulation scheme. This study presents a one-dimensional (1D) space–time signal processing scheme for the joint estimation of direction of arrival (DOA) and carrier frequency offsets [...] Read more.
The orthogonal frequency division multiple access (OFDMA) technique has been widely employed in sensor networks as the data modulation scheme. This study presents a one-dimensional (1D) space–time signal processing scheme for the joint estimation of direction of arrival (DOA) and carrier frequency offsets (CFOs) in OFDMA uplink systems. The proposed approach, initiated by a one-dimensional ESPRIT algorithm, involves estimating the DOAs of the received signal to identify subscriber positions. Spatial beamformers are then used to suppress multiple access interference and separate each subscriber’s signal from the received signal. The outputs of the spatial beamformer are decimated to estimate the CFO of each subscriber. Compared with conventional two-dimensional parameter estimation algorithms, the proposed one-dimensional algorithm has a higher estimation accuracy and significantly lower computational complexity. Full article
(This article belongs to the Special Issue Signal Processing for Wireless Sensor Networks and Communication System)
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