Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning based Per-antenna Discrete Power Control for
  Massive MIMO Systems   Power consumption is one of the major issues in massive MIMO (multiple input
multiple output) systems, causing increased long-term operational cost and
overheating issues. In this paper, we consider per-antenna power allocation
with a given finite set of power levels towards maximizing the long-term energy
efficiency of the multi-user systems, while satisfying the QoS (quality of
service) constraints at the end users in terms of required SINRs
(signal-to-interference-plus-noise ratio), which depends on channel
information. Assuming channel states to vary as a Markov process, the
constraint problem is modeled as an unconstraint problem, followed by the power
allocation based on Q-learning algorithm. Simulation results are presented to
demonstrate the successful minimization of power consumption while achieving
the SINR threshold at users.

27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat