Safe RAN control: A Symbolic Reinforcement Learning Approach
  In this paper, we present a Symbolic Reinforcement Learning (SRL) based
architecture for safety control of Radio Access Network (RAN) applications. In
particular, we provide a purely automated procedure in which a user can specify
high-level logical safety specifications for a given cellular network topology
in order for the latter to execute optimal safe performance which is measured
through certain Key Performance Indicators (KPIs). The network consists of a
set of fixed Base Stations (BS) which are equipped with antennas, which one can
control by adjusting their vertical tilt angle. The aforementioned process is
called Remote Electrical Tilt (RET) optimization. Recent research has focused
on performing this RET optimization by employing Reinforcement Learning (RL)
strategies due to the fact that they have self-learning capabilities to adapt
in uncertain environments. The term safety refers to particular constraints
bounds of the network KPIs in order to guarantee that when the algorithms are
deployed in a live network, the performance is maintained. In our proposed
architecture the safety is ensured through model-checking techniques over
combined discrete system models (automata) that are abstracted through the
learning process. We introduce a user interface (UI) developed to help a user
set intent specifications to the system, and inspect the difference in agent
proposed actions, and those that are allowed and blocked according to the
safety specification.
