Title :
Approximations of EESM Effective SNR Distribution
Author :
Song, Hui ; Kwan, Raymond ; Zhang, Jie
Author_Institution :
Centre for Wireless Network Design, Univ. of Bedfordshire, Luton, UK
fDate :
2/1/2011 12:00:00 AM
Abstract :
The Probability Density Function (PDF) or Cumulative Distribution Function (CDF) of the effective Signal to Noise Ratio (SNR) is an important statistical characterization in the performance analysis of an Orthogonal Frequency Division Multiple Access (OFDMA) system using Exponential Effective SNR Mapping (EESM). However, the exact closed form of PDF is extremely difficult to obtain. A general approximation method known as Moment Matching Approximating (MMA) is used to approximate the distribution of effective SNR by a simple expression. In this paper, the approximation by Gaussian, Generalized Extreme Value (GEV) and Pearson distribution are studied. Results show that Gaussian approximation is very useful when the number of sub-carriers is sufficiently large. Both GEV and Pearson approximation are accurate enough in approximating the distribution of effective SNR in a general case.
Keywords :
Gaussian distribution; Long Term Evolution; OFDM modulation; frequency division multiple access; Gaussian approximation; Long Term Evolution; Pearson distribution; cumulative distribution function; exponential effective SNR mapping; generalized extreme value; moment matching approximating; orthogonal frequency division multiple access; probability density function; signal to noise ratio; Exponential effective SNR mapping (EESM); Gaussian distribution; Pearson system; channel feedback; generalized extreme value (GEV) distribution; long term evolution (LTE); moment matching approximating (MMA); orthogonal frequency division multiple access (OFDMA);
Journal_Title :
Communications, IEEE Transactions on
DOI :
10.1109/TCOMM.2011.011811.100056