Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Asymptotically Optimal Approximation of the Conditional Mean Channel
  Estimator based on Gaussian Mixture Models   This paper investigates a channel estimator based on Gaussian mixture models
(GMMs) in the context of linear inverse problems with additive Gaussian noise.
We fit a GMM to given channel samples to obtain an analytic probability density
function (PDF) which approximates the true channel PDF. Then, a conditional
mean estimator (CME) corresponding to this approximating PDF is computed in
closed form and used as an approximation of the optimal CME based on the true
channel PDF. This optimal CME cannot be calculated analytically because the
true channel PDF is generally unknown. We present mild conditions which allow
us to prove the convergence of the GMM-based CME to the optimal CME as the
number of GMM components is increased. Additionally, we investigate the
estimator's computational complexity and present simplifications based on
common model-based insights. Further, we study the estimator's behavior in
numerical experiments including multiple-input multiple-output (MIMO) and
wideband systems.

20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat