Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Random Forest Resource Allocation for 5G Systems: Performance and
  Robustness Study   Next generation cellular networks will have to leverage large cell
densifications to accomplish the ambitious goals for aggregate multi-user sum
rates, for which CRAN architecture is a favored network design. This shifts the
attention back to applicable resource allocation (RA), which need to be
applicable for very short radio frames, large and dense sets of radio heads,
and large user populations in the coordination area. So far, mainly CSI-based
RA schemes have been proposed for this task. However, they have considerable
complexity and also incur a significant CSI acquisition overhead on the system.
In this paper, we study an alternative approach which promises lower complexity
with also a lower overhead. We propose to base the RA in multi-antenna CRAN
systems on the position information of user terminals only. We use Random
Forests as supervised machine learning approach to determine the multi-user
RAs. This likely leads to lower overhead costs, as the acquisition of position
information requires less radio resources in comparison to the acquisition of
instantaneous CSI. The results show the following findings: I) In general,
learning-based RA schemes can achieve comparable spectral efficiency to
CSI-based scheme; II) If taking the system overhead into account,
learning-based RA scheme utilizing position information outperform legacy
CSI-based scheme by up to 100%; III) Despite their dependency on the training
data, Random Forests based RA scheme is robust against position inaccuracies
and changes in the propagation scenario; IV) The most important factor
influencing the performance of learning-based RA scheme is the antenna
orientation, for which we present three approaches that restore most of the
original performance results. To the best of our knowledge, these insights are
new and indicate a novel as well as promising approach to master the complexity
in future cellular networks.

20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc