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Mathematics
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Intelligent Signal Processing and Intelligent Communication
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Intelligent Signal Processing and Intelligent Communication

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 2620

Special Issue Editors

Prof. Dr. Yulong Gao

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: machine learning; deep learning
Dr. Ruoyu Zhang

E-Mail Website
Guest Editor
Key Laboratory of Near-Range RF Sensing ICs & Microsystems, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: integrated sensing and communication; massive MIMO; millimeter-wave communications; sparse signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Informatization and digitalization are crucial to the future development of technology, and communication and signal processing technology are both foundational issues. However, they are currently facing enormous challenges arising from the rapid proliferation of their applicative scenarios. Fortunately, intelligent technology provides effective solutions and tools for addressing these challenges. At present, AI technology has been applied to numerous fields of communication and signal processing, and many achievements have been realized.   However, there are still many key problems to be solved, such as spectrum management and control, communication resource allocation, network management and optimization, physical layer technology, the estimation of signal parameters, multi-source information fusion and multiple signal separation. Therefore, we are organizing this Special Issue addressing intelligent communication and signal processing in order to stimulate everyone's creativity and provide a platform for innovative ideas.

Prof. Dr. Yulong Gao
Dr. Ruoyu Zhang
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. Mathematics 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

  • intelligent management and control of the spectrum
  • intelligent communication resource allocation
  • intelligent network management and optimization
  • intelligent physical layer technology
  • intelligent estimation of signal parameters
  • intelligent multi-source information fusion
  • intelligent multiple signal separation

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Published Papers (2 papers)

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Research

16 pages, 2570 KiB  
Article
A Subspace-Based Frequency Synchronization Algorithm for Multicarrier Communication Systems
by Yung-Yi Wang and Shih-Jen Yang
Mathematics 2024, 12(16), 2568; https://doi.org/10.3390/math12162568 - 20 Aug 2024
Viewed by 749
Abstract
We present a subspace-based polynomial rooting algorithm to estimate the frequency bias (FB) of generalized frequency division multiplexing (GFDM) systems employing null subcarriers and repetitive sub-symbols. The estimation process is classified into fractional FB (FFB) and integer FB (IFB) estimation. The use of [...] Read more.
We present a subspace-based polynomial rooting algorithm to estimate the frequency bias (FB) of generalized frequency division multiplexing (GFDM) systems employing null subcarriers and repetitive sub-symbols. The estimation process is classified into fractional FB (FFB) and integer FB (IFB) estimation. The use of repetitive sub-symbols creates a quasi-periodic structure in the FB-distorted received signal, allowing the proposed algorithm to estimate the FFB using the root-MUSIC algorithm. Based on this, the proposed algorithm compensates for the FFB in the received signal and then estimates the null subcarrier pattern (NSP) in the frequency domain. As a result, the IFB estimate can be obtained in a maximum likelihood (ML) manner. Before the NSP estimation, this study uses a sub-symbol combiner to enhance signal strength of the FFB-aligned signal, ensuring the reliability of the IFB estimate. Computer simulations show that the proposed subspace-based algorithm has several advantages over traditional FB estimation methods: 1. Unlike some existing algorithms that use a training sequence to estimate FB, the proposed approach is a semi-blind algorithm because it can deliver information through repeated sub-symbols while estimating FB; 2. The proposed algorithm demonstrates excellent estimation accuracy compared to most traditional FB estimation algorithms; and 3. The proposed algorithm is computationally efficient, making it applicable to real-time applications in future communication systems. Full article
(This article belongs to the Special Issue Intelligent Signal Processing and Intelligent Communication)
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14 pages, 2966 KiB  
Article
Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise
by Junyan Cui, Wei Pan and Haipeng Wang
Mathematics 2024, 12(6), 832; https://doi.org/10.3390/math12060832 - 12 Mar 2024
Cited by 2 | Viewed by 1287
Abstract
In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) [...] Read more.
In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) estimation of signals in impulsive noise. Firstly, the algorithm performs eigenvalue decomposition on the received covariance matrix to obtain eigenvectors and eigenvalues, and then the Toeplitz matrix is created according to the eigenvectors corresponding to its eigenvalues. Secondly, the spatial averaging method is used to obtain an unbiased estimate of the Toeplitz matrix, which is then weighted and added based on the corresponding eigenvalues. Next, the noise subspace of the Toeplitz matrix is reconstructed to obtain the one that has less angle information. Finally, the DOA of the coherent signal is estimated using the Multiple Signal Classification (MUSIC) algorithm. The improved method based on the Toeplitz matrix can not only suppress the effect of impulsive noise but can also solve the problem of aperture loss due to its decoherence. A series of simulations have shown that they have better performances than other algorithms. Full article
(This article belongs to the Special Issue Intelligent Signal Processing and Intelligent Communication)
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