Fifth-generation (5G) cellular networks promise unprecedented connectivity through ultra-low latency and high-speed mobile broadband, driving the need for intelligent slice placement strategies in Open Radio Access Network (O-RAN) architectures. O-RAN promotes openness and vendor interoperability, but existing Machine Learning (ML)-based embedding solutions often prioritize performance metrics such as delay and availability while neglecting energy efficiency. To address this gap, we propose Crown, a Reinforcement Learning (RL)-based service placement framework that jointly optimizes Service Level Agreement (SLA) compliance and power consumption. Crown extends a traditional Deep Q-Network (DQN) by integrating cross-attention layers to model complex dependencies between virtualized O-RAN functions and heterogeneous physical servers, enabling more informed placement decisions. We evaluate Crown in a simulated O-RAN environment and compare it against state-of-the-art RL approaches and heuristic baselines. Results demonstrate that Crown reduces power consumption by 57% compared to a fixed deployment and by 15% relative to a DQN without cross‑attention, while meeting stringent latency and bandwidth requirements through action masking and achieving high slice admission rates and low deployment cost via its cost‑aware reward design. Furthermore, we measure the inference time, showing that the attention-enhanced RL design remains practical for large-scale deployments.
Monaco et al. (Wed,) studied this question.