Analysis of signal-to-noise ratio estimation algorithms based on inphase and quadrature components of the received signal

Аuthors

Serkin F. B.^1*, Vazhenin N. A.^**, Veytsel V. V.²

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. Organization « Topcon Positioning Systems», 7, Derbenevskaya naberezhnaya, building 22, Moscow, 115114, Russia

*e-mail: serkinfb@list.ru
**e-mail: N.Vazhenin@mai.ru

Abstract

Signal-to-noise ratio estimation plays significant role in state of art communication, navigation and location systems. Signal-to-noise ratio affect performance of these systems and its estimation can be used to control systems and adopt its characteristics for various conditions. The paper presents a comparative analysis of various signal-to-noise estimation algorithms. These algorithms based on quadrature components of the received signal. Considered the quality of operation of these algorithms in two cases: when the phase synchronization has zero error, and when there is a various fixed error. All considered algorithms can be divided into two categories: based on in-phase and quadrature components itself and based on received signal vector length. Analysis performed for channel with additive white Gaussian noise and binary phase shift keying modulation. MATLAB/Simulink software used to simulate realizations of algorithms in described specific environment. Algorithms accuracy analysis obtained for 10% maximum error. The results of the work can be concluded as follows: all considered algorithms have estimation errors for signal-to-noise ratio of less than 10 dB; the minimum level of these errors can be achieved with algorithm (2.31); algorithms, that are effective in the presence of phase synchronization error, and algorithms, that are effective in the case of zero phase synchronization error, can be selected; algorithms based on received signal vector length are resistant to phase synchronization errors, but they have highest errors in less than 10 dB area.

Keywords:

signal-to-noise estimation, simulation, MATLAB/Simulink

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