TY - JOUR
T1 - Gaussian approximation for the wireless multi-access interference distribution
AU - Inaltekin, Hazer
PY - 2012/11
Y1 - 2012/11
N2 - The problem of Gaussian approximation for the wireless multi-access interference distribution in large spatial wireless networks is addressed. First, a principled methodology is presented to establish rates of convergence of the multi-access interference distribution to a Gaussian distribution for general bounded and power-law decaying path-loss functions. The model is general enough to also include various random wireless channel dynamics such as fading and shadowing arising from multi-path propagation and obstacles existing in the communication environment. It is shown that the wireless multi-access interference distribution converges to the Gaussian distribution with the same mean and variance at a rate 1√λ, where λ > 0 is a parameter controlling the intensity of the planar (possibly non-stationary) Poisson point process generating node locations. An explicit expression for the scaling coefficient is obtained as a function of fading statistics and the path-loss function. Second, an extensive numerical and simulation study is performed to illustrate the accuracy of the derived Gaussian approximation bounds. A good statistical fit between the interference distribution and its Gaussian approximation is observed for moderate to high values of λ.
AB - The problem of Gaussian approximation for the wireless multi-access interference distribution in large spatial wireless networks is addressed. First, a principled methodology is presented to establish rates of convergence of the multi-access interference distribution to a Gaussian distribution for general bounded and power-law decaying path-loss functions. The model is general enough to also include various random wireless channel dynamics such as fading and shadowing arising from multi-path propagation and obstacles existing in the communication environment. It is shown that the wireless multi-access interference distribution converges to the Gaussian distribution with the same mean and variance at a rate 1√λ, where λ > 0 is a parameter controlling the intensity of the planar (possibly non-stationary) Poisson point process generating node locations. An explicit expression for the scaling coefficient is obtained as a function of fading statistics and the path-loss function. Second, an extensive numerical and simulation study is performed to illustrate the accuracy of the derived Gaussian approximation bounds. A good statistical fit between the interference distribution and its Gaussian approximation is observed for moderate to high values of λ.
UR - http://www.scopus.com/inward/record.url?scp=84867502598&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP110102729
U2 - 10.1109/TSP.2012.2212014
DO - 10.1109/TSP.2012.2212014
M3 - Article
AN - SCOPUS:84867502598
VL - 60
SP - 6114
EP - 6120
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 11
ER -