Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the MMSE Channel Estimator   We present a method for estimating conditionally Gaussian random vectors with
random covariance matrices, which uses techniques from the field of machine
learning. Such models are typical in communication systems, where the
covariance matrix of the channel vector depends on random parameters, e.g.,
angles of propagation paths. If the covariance matrices exhibit certain
Toeplitz and shift-invariance structures, the complexity of the MMSE channel
estimator can be reduced to O(M log M) floating point operations, where M is
the channel dimension. While in the absence of structure the complexity is much
higher, we obtain a similarly efficient (but suboptimal) estimator by using the
MMSE estimator of the structured model as a blueprint for the architecture of a
neural network. This network learns the MMSE estimator for the unstructured
model, but only within the given class of estimators that contains the MMSE
estimator for the structured model. Numerical simulations with typical spatial
channel models demonstrate the generalization properties of the chosen class of
estimators to realistic channel models.

21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn