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Journal Achievements of Modern Radioelectronics №10 for 2016 г.
Article in number:
Distortion effect analysis in digital communication systems with QPSK modulation
Keywords: QPSK noise immunity signal constellation distortion analysis quadrature receiver Simulink
Authors:
S.F. Boev - Dr.Sc. (Econ.), Professor, General Director, OJSC «RTI» E-mail: kantselariya@oaorti.ru А.L. Priorov - Dr.Sc. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University E-mail: andcat@yandex.ru М.А. Dubov - Ph.D. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University E-mail: m.dubov@uniyar.ac.ru А.Е. Kiselnikov - Post-graduate Student, Leading Electronics, P.G. Demidov Yaroslavl State University E-mail: a.kiselnikov@uniyar.ac.ru К.S. Krasavin - Master Student, P.G. Demidov Yaroslavl State University E-mail: krasskir93@gmail.com
Abstract:
The scope of this work is a distortion effect analysis of BPSK and QPSK signal constellation. The paper analyzes the causes of distortions, given their mathematical description, and compares their effect on noise immunity of communication systems. Represented the analytical expressions for noise immunity QPSK and QPSK constellation for different types of distortion and provided a method for modeling communication systems and data aggregation. Signal constellation distortion identification algorithm was developed. This algorithm works on the basis of a comprehensive assessment of the quality of the communication channel and allows you to detect the presence of distortions and identify their type in a wide range of SNR with a given probability, that allows to increase the degree of automation of the process of testing electronic equipment. This algorithm has high compatibility with modern instrumentation that allows its introduction into radio devices manufacturing without any additional cost.
Pages: 3-14
References

 

  1. Oficialnyjj sajjt kompanii KeySight www.keysight.com
  2. Oficialnyjj sajjt kompanii Rohde & Schwarz www.rohde-schwarz.com
  3. Mathuranathan V. Simulation of Digital Communication Systems Using Matlab. Second edition / Mathuranathan V. E-book, athuranathan V. at Smashwords. Published at 2013.
  4. Michael D. McKinley EVM Calculation for Broadband Modulated Signals // 64th ARFTG Conf. Dig., Orlando. Florida. 2004. S. 45-52.
  5. Lehm G. Analogovye i cifrovye filtry. Raschet i realizacija. M.: Mir. 1982.
  6. Boev N.M. Sistemy svjazi. Podvizhnye sistemy svjazi. Lekcii: uchebno-metodicheskoe posobie [EHlektronnyjj resurs]. Krasnojarsk: Sib. feder. un-t. 2013.
  7. Brjukhanov JU.A. Cifrovye cepi i signaly. JAroslavl: JArGU. 2005.
  8. Okunev JU.B. Cifrovaja peredacha informacii fazomodulirovannymi signalami. M.: Radio i svjaz. 1991.
  9. Skljar B. Cifrovaja svjaz. M.: Viljams. 2007.
  10. Martjushev JU.JU. Praktika funkcionalnogo cifrovogo modelirovanija v radiotekhnike. M.: Gorjachaja linija - Telekom. 2012.
  11. Prokis Dzh. Cifrovaja svjaz. M.: Radio i svjaz. 2000.
  12. Martirosov V.E. Optimalnyjj priem diskretnykh signalov CSPI. M.: Radiotekhnika. 2010.
  13. Kiselnikov A.E., Dubov M.A., Priorov A.L. Razrabotka algoritma detektirovanija i kompensacii iskazhenija signalnogo sozvezdija FMN-2 na osnove vektora oshibok // Cifrovaja obrabotka signalov. 2016. № 1. S. 34-39.
  14. Rao C.R. Handbook of statistics. V. 24. Data mining and data visualization. ELSEVIER B.V. 2005.
  15. Dubov M.A. Modelirovanie cifrovogo radiopriemnogo ustrojjstva MV/DMV diapazona // DSPA 2010. Moskva. IPU RAN 2010. S. 110-113.
  16. SHirjaev A.N. Verojatnost. M.: Nauka. 1989.