Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs

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.