Estimation of Two-dimensional Class A Noise Model Parameters By Markov Chain Monte Carlo

Abstract

Antenna arrays are widely employed in communication systems, because the performance improvements over single antenna systems. The noise in multiple antennas may be statistically dependent from antenna to antenna and may be non-Gaussian. In this paper an efficient estimation of two-dimensional version Middleton's Class A noise model parameters is derived based on Markov Chain Monte Carlo (MCMC). This estimator can estimate five-parameter and hidden states for two-dimensional Class A noise model simultaneously. Simulation of this estimator indicates that this considered estimator is fast converges and low complexity for small data samples.