  In virtualized radio access network (vRAN), the base station (BS) functions
are decomposed into virtualized components that can be hosted at the
centralized unit or distributed units through functional splits. Such
flexibility has many benefits; however, it also requires solving the problem of
finding the optimal splits of functions of the BSs in such a way that minimizes
the total network cost. The underlying vRAN system is complex and precise
modelling of it is not trivial. Formulating the functional split problem to
minimize the cost results in a combinatorial problem that is provably NP-hard,
and solving it is computationally expensive. In this paper, a constrained deep
reinforcement learning (RL) approach is proposed to solve the problem with
minimal assumptions about the underlying system. Since in deep RL, the action
selection is the outcome of inference of a neural network, it can be done in
real-time while training to update the neural networks can be done in the
background. However, since the problem is combinatorial, even for a small
number of functions, the action space of the RL problem becomes large.
Therefore, to deal with such a large action space, a chain rule-based
stochastic policy is exploited in which a long short-term memory (LSTM)
network-based sequence-to-sequence model is applied to estimate the policy that
is selecting the functional split actions. However, the utilized policy is
still limited to an unconstrained problem, and each split decision is bounded
by vRAN's constraint requirements. Hence, a constrained policy gradient method
is leveraged to train and guide the policy toward constraint satisfaction.
Further, a search strategy by greedy decoding or temperature sampling is
utilized to improve the optimality performance at the test time. Simulations
are performed to evaluate the performance of the proposed solution using
synthetic and real network datasets.
