• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Maximum Likelihood SNR Estimation for QAM Signals Over Slow Flat Fading Rayleigh Channel

Vol. 10, No. 11, November 29, 2016
10.3837/tiis.2016.11.009, Download Paper (Free):

Abstract

Estimation of signal-to-noise ratio (SNR) is an important problem in wireless communication systems. It has been studied for various constellation types and channels using different estimation techniques. Maximum likelihood estimation is a technique which provides efficient and in most cases unbiased estimators. In this paper, we have applied maximum likelihood estimation for systems employing square or cross QAM signals which are undergoing slow flat Rayleigh fading. The problem has been considered under various scenarios like data-aided (DA), non-data-aided (NDA) and partially data-aided (PDA) and the performance of each type of estimator has been evaluated and compared. It has been observed that the performance of DA estimator is best due to usage of pilot symbols, with the drawback of greater bandwidth consumption. However, this can be catered for by using partially data-aided estimators whose performance is better than NDA systems with some extra bandwidth requirement.


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Cite this article

[IEEE Style]
N. Ishtiaq and S. A. Sheikh, "Maximum Likelihood SNR Estimation for QAM Signals Over Slow Flat Fading Rayleigh Channel," KSII Transactions on Internet and Information Systems, vol. 10, no. 11, pp. 5365-5380, 2016. DOI: 10.3837/tiis.2016.11.009.

[ACM Style]
Nida Ishtiaq and Shahzad A. Sheikh. 2016. Maximum Likelihood SNR Estimation for QAM Signals Over Slow Flat Fading Rayleigh Channel. KSII Transactions on Internet and Information Systems, 10, 11, (2016), 5365-5380. DOI: 10.3837/tiis.2016.11.009.

[BibTeX Style]
@article{tiis:21273, title="Maximum Likelihood SNR Estimation for QAM Signals Over Slow Flat Fading Rayleigh Channel", author="Nida Ishtiaq and Shahzad A. Sheikh and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2016.11.009}, volume={10}, number={11}, year="2016", month={November}, pages={5365-5380}}