Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs. (arXiv:2106.00011v1 [cs.NI]) Leave a comment

Virtualized Radio Access Network (vRAN) brings agility to Next-Generation RAN
through functional split. It allows decomposing the base station (BS) functions
into virtualized components and hosts it either at the distributed-unit (DU) or
central-unit (CU). However, deciding which functions to deploy at DU or CU to
minimize the total network cost is challenging. In this paper, a constrained
deep reinforcement based functional split optimization (CDRS) is proposed to
optimize the locations of functions in vRAN. Our formulation results in a
combinatorial and NP-hard problem for which finding the exact solution is
computationally expensive. Hence, in our proposed approach, a policy gradient
method with Lagrangian relaxation is applied that uses a penalty signal to lead
the policy toward constraint satisfaction. It utilizes a neural network
architecture formed by an encoder-decoder sequence-to-sequence model based on
stacked Long Short-term Memory (LSTM) networks to approximate the policy.
Greedy decoding and temperature sampling methods are also leveraged for a
search strategy to infer the best solution among candidates from multiple
trained models that help to avoid a severe suboptimality. Simulations are
performed to evaluate the performance of the proposed solution in both
synthetic and real network datasets. Our findings reveal that CDRS successfully
learns the optimal decision, solves the problem with the accuracy of 0.05\%
optimality gap and becomes the most cost-effective compared to the available
RAN setups. Moreover, altering the routing cost and traffic load does not
significantly degrade the optimality. The results also show that all of our
CDRS settings have faster computational time than the optimal baseline solver.
Our proposed method fills the gap of optimizing the functional split offering a
near-optimal solution, faster computational time and minimal hand-engineering.

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