Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Matrix Design for 2D DoA Estimation with Compressive Antenna
  Arrays using Stochastic Gradient Descent   Recently, compressive antenna arrays have been considered for DoA estimation
with reduced hardware complexity. By utilizing compressive sensing, such arrays
employ a linear combining network to combine signals from a larger set of
antenna elements in the analog RF domain. In this paper, we develop a design
approach based on the minimization of error between spatial correlation
function (SCF) of the compressive and the uncompressed array resulting in the
estimation performance of the two arrays to be as close as possible. The
proposed design is based on grid-free stochastic gradient descent (SGD)
optimization. In addition to a low computational cost for the proposed method,
we show numerically that the resulting combining matrices perform better than
the ones generated by a previous approach and combining matrices generated from
a Gaussian ensemble.

5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph