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Advances in Digital Signal Processing and Communications

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: 30 June 2026 | Viewed by 585

Special Issue Editor


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Guest Editor
Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi-si 39177, Republic of Korea
Interests: signal processing; adaptive filter; deep learning

Special Issue Information

Dear Colleagues,

The accelerating pace of technological advancement and the growing demands of users are continuously driving innovation across various systems and technical domains. This rapid progress simultaneously introduces new challenges related to processing capability, efficiency, and accessibility, necessitating the development of novel ideas and state-of-the-art solutions.

This Special Issue welcomes the submission of high-quality research papers that present new theoretical insights, innovative algorithmic developments, and validated experimental results in the field of "Advances in Digital Signal Processing and Communications". The goal of this Special Issue is to highlight cutting-edge research that addresses critical signal processing challenges across all layers of modern communication systems, particularly at the physical layer.

Dr. Jaejin Jeong
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 2400 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

  • adaptive filtering algorithms
  • digital modulation techniques
  • MIMO systems and signal processing
  • signal sampling and quantization
  • noise and interference mitigation
  • machine learning for signal processing
  • real-time signal processing
  • 5G and wireless communications
  • edge computing for signal processing

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Published Papers (1 paper)

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Research

15 pages, 1409 KB  
Article
Input Noise Variance Estimation for Adaptive Filtering Without Prior Information
by Jae Jin Jeong
Appl. Sci. 2026, 16(8), 3780; https://doi.org/10.3390/app16083780 - 13 Apr 2026
Viewed by 310
Abstract
In adaptive filtering, input noise degrades the performance of standard algorithms by introducing bias into the weight estimate. This paper presents a bias-compensated adaptive filtering method that estimates the input noise variance without requiring any prior information. By exploiting the orthogonality between the [...] Read more.
In adaptive filtering, input noise degrades the performance of standard algorithms by introducing bias into the weight estimate. This paper presents a bias-compensated adaptive filtering method that estimates the input noise variance without requiring any prior information. By exploiting the orthogonality between the estimated weight vector and the error vector, the proposed Prior-Free Noise Variance Estimator (PFNVE) obtains a reliable noise variance estimate that is independent of the output noise characteristics and the input-to-output noise ratio. System identification experiments show that the PFNVE achieves steady-state accuracy comparable to existing techniques while offering noticeably faster tracking when the system undergoes abrupt changes. Since the PFNVE does not require any structural modification to the underlying filter, it can be directly applied to other adaptive filters, including MS-PNLMS. Performance is evaluated through simulations on dense and sparse systems under various conditions, including colored input and time-varying noise. Full article
(This article belongs to the Special Issue Advances in Digital Signal Processing and Communications)
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