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Entropy
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Information Theory in Multimedia Systems and Signal Processing
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Information Theory in Multimedia Systems and Signal Processing

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (23 May 2023) | Viewed by 2227

Special Issue Editor

Prof. Dr. Jaeyoung Choi

E-Mail Website
Guest Editor
Department of AI Software, Gachon University, Seongnam-si 13120, Korea
Interests: big data analytics; multimedia networking; signal processing; statistical inference
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Entropy is one of the most useful topics in various domains, such as computer science and artificial intelligence. Most specifically, in the multimedia system and signal processing, entropy has become an important measure, e.g., in relation to understanding fundamental tradeoffs between competing resource requirements, developing practical techniques and heuristics for realizing complicated optimization and allocation strategies, and demonstrating innovative mechanisms and frameworks for large-scale multimedia applications. To this end, both theory and practice in various heterogeneous and inter-related fields, including image, video, and audio data processing, as well as new sources of multimodal data (text, social, health, etc.) are used. In this Special Issue, we focus on the analysis, design, and implementation of multimedia systems and signal processing with various tools in information theory.

Prof. Dr. Jaeyoung Choi
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 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. Entropy is an international peer-reviewed open access monthly 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

  • information theory
  • entropy
  • multimedia communications and networking
  • multimedia systems and applications for Internet of Things
  • Big Data analytics for multimedia
  • machine learning for multimedia
  • entropy-based video coding
  • social/health multimedia
  • image and video processing, compression, and segmentation
  • deep learning for signal processing

Published Papers (1 paper)

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Research

20 pages, 2900 KiB  
Article
Discrete Phase Shifts of Intelligent Reflecting Surface Systems Considering Network Overhead
by Jaehong Kim, Heejung Yu, Xin Kang  and Jingon Joung 
Entropy 2022, 24(12), 1753; https://doi.org/10.3390/e24121753 - 30 Nov 2022
Cited by 5 | Viewed by 1854
Abstract
In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal [...] Read more.
In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20% of coverage extension can be accomplished by deploying IRS with the proposed method. Full article
(This article belongs to the Special Issue Information Theory in Multimedia Systems and Signal Processing)
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