Special Issue "Information Theory Applied to Communications and Networking"

Guest Editor
Prof. Dr. Eduard Jorswieck
TU Dresden, Institut für Nachrichtentechnik, Lehrstuhl Theoretische Nachrichtentechnik, 01062 Dresden, Germany
Website: http://www.ifn.et.tu-dresden.de/tnt/jorswieck/document_view?set_language=en
E-Mail: eduard.jorswieck@tu-dresden.de
Interests: signal processing for communications and networks; applied information theory; communications theory

Guest Editor
Prof. Dr. Mikael Skoglund
School of Electrical Engineering, Communication Theory, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Website: http://www.ee.kth.se/~skoglund/
E-Mail: skoglund@ee.kth.se
Interests: information theory; communication theory; wireless communications

Dear Colleagues,

The special issue focuses on contributions based on Shannon's information concepts applied to problems in communications and networking.

When Shannon introduced his version of "entropy" and the related concept of "mutual information", he had problems in electrical communication in mind. Since then his theory has found a wide range of applications also outside the central field of telecommunications. The goal of this special issue is however to provide a modern view on problems in communications and networking, and the use of Shannon's notions to understand and characterize fundamental opportunities and limitations.

Prospective contributions should consider theory and problems motivated by the wide area of communications and networking. Shannon's original concepts of entropy and/or mutual information should be of central importance.

Prof. Dr. Eduard Jorswieck
Prof. Dr. Mikael Skoglund
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 1200 CHF (Swiss Francs).

• Shannon entropy
• mutual information
• coding theorems
• capacity
• communication networks
• detection and estimation

Type of Paper: Article
Title: Performance of Alamouti MIMO Systems in Challenging Environment
Authors: Józef Pawelec¹ and Krzysztof Kosmowski²
Affiliations: ¹ Pulaski University of Technology, j.pawelec@pr.radom.pl
² Military Communications’ Institute, k.kosmowski@wil.waw.pl
Abstract: The paper deals with multiple input – multiple output systems using the Alamouti antennae architecture and space-time coding technique. The basic theory is given as well as numerous simulation data. The main contributions are as follows:
- The Alamouti MIMO system of 2x2 antennas offers a gain of the order of 30 dB in a quasi static conditions, e.i. where the rate of transmission does not approach    the fading rate R
- The channel cross-correlation effect is negligible as far as the correlation coefficient in any pair of channels does not exceed the value of 0.5
- The joint action of both factors – the cross-correlation above 0.5 and Doppler bandwith F_D@ R causes much hazard approaching previous gain
Keywords: MIMO systems, Alamouti architecture, cross-correlation, fast fading

Type of Paper: Article
Title: Features Extraction Method for Brain-Machine Communication Based on the Empirical Mode Decomposition
Authors: Pablo F. Diez¹, Vicente A. Mut², Eric Laciar¹, Abel Torres³ and Enrique M. Avila Perona²
Affiliations: ¹ Gabinete de Tecnología Médica, Universidad Nacional de San Juan, Av. San Martín 1109 (oeste), Capital, San Juan (J5400ARL), Argentina; E-Mail: pdiez@gateme.unsj.edu.ar
² Instituto de Automática, Universidad Nacional de San Juan, San Juan, Argentina; E-Mails: vmut@inaut.unsj.edu.ar (V.A.M.); eavila@inaut.unsj.edu.ar (E.M.A.P.)
³Dept. ESAII, Universitat Politècnica de Catalunya, Institut de Bioenginyeria de Catalunya (IBEC) and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
Abstract: A Brain-Computer Interface (BCI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a computer or a machine. It is proposed a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. This is an important part in the development of BCI. The Empirical Mode Decomposition (EMD) is a method capable of processing non-stationary and non-linear signals, as the EEG. It was applied on EEG signals of 7 subjects performing 5 mental tasks. Six features were computed, namely, Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies. In order to reduce the dimensionality of the feature vector the Wilks’ lambda parameter, was used for the selection of the most important variables. The classification of mental tasks was performed using Linear Discriminate Analysis (LDA) and Neural Networks (NN). Using this method, the average classification over all subjects in database is 91±5% and 87±5% using LDA and NN, respectively. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods, like as spectral analysis.
Keywords: brain computer interface (BCI); empirical mode decomposition (EMD); feature extraction