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Information Theory and Game Theory

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 (20 January 2022) | Viewed by 2347

Special Issue Editor


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Guest Editor
Institute for Communications Technology, Technische Universität Braunschweig, 38106 Braunschweig, Germany
Interests: applied information theory; applied game theory; communications theory; signal processing for communications and networks; optimization theory; statistical signal processing

Special Issue Information

Dear Colleagues,

Information theory builds the basis for the digitalization of our current world. All forms of data creation, storage, transmission, processing, and data consumption are well mathematically modeled. Complex systems and networks comprise multiple nodes and concurring interests in natural as well as adversarial setting. Game theory provides a solid basis for the modeling and systematic solution of conflict situations of rational agents.

Both methods have worked fruitfully together to analyze and design complex communication systems and networks. Examples include resource allocation problems in multiuser scheduling networks, power control in interference networks, or distributed beamforming, which comprises networks operating in secure environments where both legitimate nodes and attackers compete against each other. Finally, other examples include game models for quantum computing and communications where quantum states are maintained as a valuable resource in communication networks.

The Special Issue solicits contributions at the interdisciplinary intersection between information and game theory. Both fundamental contributions to improve the understanding of the interplay between information and game theory as well as applied work based on these disciplines are of interest.    

Prof. Dr. Eduard Axel Jorswieck
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
  • game theory
  • optimization
  • resource allocation
  • scheduling
  • communications
  • networks
  • power control
  • beamforming
  • quantum communications

Published Papers (1 paper)

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Research

22 pages, 1322 KiB  
Article
Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks
by Jinxi Zhang, Gang Chuai and Weidong Gao
Entropy 2022, 24(2), 300; https://0-doi-org.brum.beds.ac.uk/10.3390/e24020300 - 20 Feb 2022
Cited by 7 | Viewed by 1884
Abstract
Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works [...] Read more.
Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity. Full article
(This article belongs to the Special Issue Information Theory and Game Theory)
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