Title :
Weak Convergence and Rate of Convergence of MIMO Capacity Random Variable
Author :
Raghavan, Vasanthan ; Sayeed, Akbar M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI
Abstract :
Recent works on the distribution function of the capacity of independent and identically distributed (i.i.d.) and semicorrelated narrowband channels show that the outage capacity computed using a Gaussian approximation is close to the true outage capacity even for few antennas. Motivated by physical scattering considerations, we study a multi-antenna channel model with independent entries that are not necessarily identically distributed and show the weak convergence of capacity to a Gaussian random variable. The channel model considered in this paper subsumes well-studied cases like the i.i.d. and separable correlation models and thus we generalize previous results on weak convergence of multi-antenna capacity. Using recent results from random matrix theory, we also study the rate of convergence of ergodic capacity of i.i.d. channels to its limit value. Employing a well-known conjecture from random matrix theory, we establish tight results for the rate of convergence and show a dependence of this rate on signal-to-noise ratio
Keywords :
Gaussian channels; MIMO systems; antenna arrays; approximation theory; channel capacity; convergence of numerical methods; correlation theory; electromagnetic wave scattering; matrix algebra; Gaussian approximation; MIMO capacity; convergence rate; distribution function; ergodic capacity; multiantenna channel model; random matrix theory; scattering consideration; semicorrelated narrowband channel; Channel capacity; Convergence; Distributed computing; Distribution functions; Gaussian approximation; MIMO; Narrowband; Random variables; Scattering; Signal to noise ratio; Canonical statistical model; empirical eigenvalue distribution; ergodic capacity; multiple-input–multiple-output (MIMO) capacity; outage capacity; random matrix theory; scattering; virtual representation;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2006.878216