Data Analytics and Optimization for Hybrid Communication Systems

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 September 2016) | Viewed by 4344

Special Issue Editor

1. OPTIMA Area, TECNALIA, Basque Research & Technology Alliance (BRTA), 48160 Zamudio, Bizkaia, Spain
2. Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Bizkaia, Spain
Interests: machine learning; deep learning; meta-heuristic optimization; explainable artificial intelligence; responsible artificial intelligence; stream learning
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Special Issue Information

Dear Colleagues,

The last decade has experienced an upsurge of contributions aimed at validating the applicability of novel supervised learning models, clustering schemes and optimization algorithms to problems arising from a wide variety of fields. One such application scenario gravitates on the different paradigms that stem from the optimized management and operation of next-generation communication systems, especially in what regards the deployment of hybrid WAN/LAN/BAN wired/wireless networks, the incorporation of highly-directive transmission technologies, such as mmWave communications, the exploitation of massively captured information by such networks (coming either from its own operational processes or from the users of the network itself) and the consideration of energy efficiency in the allocation of radio resources. In all such paradigms, the use of novel optimization approaches (including the latest advances in swarm intelligence and bioinspired computation) and predictive modeling are called to play a crucial role to cope with the intrinsic challenges of an optimized, adaptive and efficient management and operation of such hybrid networks.

This rationale motivates the launch of a Special Issue of Algorithms is devoted to technical  contributions in the wide fields of data analytics (clustering, predictive modeling) and optimization (with a focus on bioinspired solvers), applied to the management and optimization of hybrid communication systems. Specific applications of interest include, but are not limited to:

  • Hybrid wireless/wired networking
  • mmWave/uWave communications
  • Body Area Networks
  • Multiple user communications, including interference avoidance/alignment
  • Synchronization methods
  • Radio resource management (e.g., spectrum, power)
  • Channel estimation and equalization
  • Source/channel coding/decoding
  • Data-based routing protocols
  • Energy-efficient network topologies
  • Dynamic resource allocation
  • Data fusion techniques
  • Cross-layer synergies among physical and upper (e.g., network) layers
  • Implementation issues
  • Survivability and reliability
  • Network deployment
  • Quality of Service / Quality of Experience
  • Congestion control

Prof. Dr. Javier Del Ser Lorente
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. Algorithms 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 1600 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

  • Hybrid communication networks
  • Resource allocation
  • Bioinspired optimization and data analytics

Published Papers (1 paper)

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Article
Control for Ship Course-Keeping Using Optimized Support Vector Machines
by Weilin Luo and Hongchao Cong
Algorithms 2016, 9(3), 52; https://doi.org/10.3390/a9030052 - 10 Aug 2016
Cited by 7 | Viewed by 3886
Abstract
Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account [...] Read more.
Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller. Full article
(This article belongs to the Special Issue Data Analytics and Optimization for Hybrid Communication Systems)
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