CROWN: Cross-attention reinforcement learning for O-RAN wireless networks

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.