Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Variational Inference based Detection Method for Repetition Coded
  Generalized Spatial Modulation   In this paper, we consider a simple coding scheme for spatial modulation
(SM), where the same set of active transmit antennas is repeatedly used over
consecutive multiple transmissions. Based on a Gaussian approximation, an
approximate maximum likelihood (ML) detection problem is formulated to detect
the indices of active transmit antennas. We show that the solution to the
approximate ML detection problem can achieve a full coding gain. Furthermore,
we develop a low-complexity iterative algorithm to solve the problem with low
complexity based on a well-known machine learning approach, i.e., variational
inference. Simulation results show that the proposed algorithm can have a near
ML performance. A salient feature of the proposed algorithm is that its
complexity is independent of the number of active transmit antennas, whereas an
exhaustive search for the ML problem requires a complexity that grows
exponentially with the number of active transmit antennas.

28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an