METHODS AND ALGORITHMS OF SOFT DECODING FOR SIGNALS WITHIN INFORMATION TRANSMISSION CHANNELS BETWEEN CONTROL SYSTEMS ELEMENTS
DOI:
https://doi.org/10.15588/1607-3274-2018-4-22Keywords:
channel symbol, decoding, decision making, fuzzy logic, simulation, quasi-optimal receivingAbstract
Context. The problems of increasing the reliability of information transmission in the channels of control systems against thebackground of the impact of a set a different nature and type interference are solved. The object of research is receiving device
(channel symbols decoder), the subjects of research are models, methods and algorithms of proceeding and decision making for elementary signals (channels symbols). The goal is creation and research of methods and algorithms of elementary signals «soft» decoding for providing of information transmission reliability in the build-in and dedicated channels of control systems.
Method. The math methods of fuzzy sets for description of errors within information transmission channels between control systems
elements is used. The methods and algorithms of «soft» decoding within first decision device of control systems elements are realized (this
methods allows get more information for decision making then «hard» method of decision making). The program simulation of proposed
method of decision making based on fuzzy logic is executed.
Results. The structure scheme and functioning algorithm of quasi-optimal receiver of elementary signal with original using methods of fuzzy logic for decision making are created. The algorithm of decision making is researched with using the simulation tool MathWorks Fuzzy Logic Toolbox.
Conclusions. The research results allows to make the next conclusions that proposed «soft» decoding method provides greater
«flexibility» for decision making that positively affects the reliability of information transmission; don’t require the introduction and
use of multi-valued logic (for example, the symbol «x» for the model of the channel 2×3, the symbols «1b» and «0b» for the model
of the channel 2×4, the symbols «0» ... «7» for the model of the channel 2×8) that facilitates the implementation of the decoding algorithm; allows to execute arithmetic (not logical) calculation for decreasing a computational difficult of decision making algorithm.
References
Baklanov I. G. Testing and diagnostics of telecommunication systems. EKO-TRENDS Publ., 2001, 265 p.
Kon E. L., Freyman V. I. Approaches to test diagnostics of digital devices, The News of Perm national research polytechnic university. Electronics, information technologies, control systems, 2012, No. 6, pp. 231–241.
Kon E. L., Freyman V. I. The theory of telecommunications. The noise stability data transmission within information and control and telecommunication systems: models, algorithms,
structures. PSTU Publ., 2007, 317 p.
Sklar B. Digital Communications. Fundamentals and Applications. Second Edition. New Jersey, Prentice Hall Ptr.
Pahomov G. I., Freyman V. I. The theory of telecommunications. The general terms. PSTU Publ., 2007, 115 p.
Fink L. M. Signals, noise, errors. Radio and communication Publ., 1984, 256 p.
Gladkih A. A. Fundamentals of the theory of soft decoding of redundant codes in the erase channel of communication. USTU Publ., 2010, 379 p.
Freyman V., Kavalerov M. Application of Fuzzy Logic for Decoding and Evaluation of Results within the Process of Information System Components Diagnosis, Proceedings of the 2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2017 ElConRus), February 1–3, 2017, pp. 134–139.
Piegat A. Fuzzy Modeling and Control / A. Piegat. Physica Verlag, A Springer Verlag Company.
Freyman V., Bezukladnikov I. Research and Application of Noise Stability Providing Methods at Information and Control Systems / V. Freyman, // Proceedings of the 2017 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference (2017 ElConRus), February 1–3, 2017, pp. 831–837.
Freyman V. I. The application of fuzzy logic for channel symbols soft decoding at the first decision node of receiving devices, Neurocomputers: designing, application, 2017, No. 6, pp. 49–54.
Shtovba S. D. Designing of fuzzy systems with using
MATLAB tools. Hotline-Telecom, 2007, 288 p.
MATLAB Documentation [electron resource]. URL:
http://www.mathworks.com/help/matlab/ (date of access: 14.04.2018).
Kon E. L., Freyman V. I., Yuzhakov A. A. Soft decoding based fuzzy logic for processing of elementary signals within data transmission channels of distributed control systems, Proceedings of the 2017 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SINKHROINFO), pp. 1–6. DOI:
1109/SINKHROINFO.2017.7997531.
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