The Open Radio Access Network (O-RAN) initiative, characterized by open interfaces and AI/ML-capable RAN Intelligent Controller (RIC), facilitates effective spectrum sharing among RANs. In this context, we introduce AdapShare, an ORAN-compatible solution leveraging Reinforcement Learning (RL) for intent-based spectrum management, with the primary goal of minimizing resource surpluses or deficits in RANs. By employing RL agents, AdapShare intelligently learns network demand patterns and uses them to allocate resources. We demonstrate the efficacy of AdapShare in the spectrum sharing scenario between LTE and NR networks, incorporating real-world LTE resource usage data and synthetic NR usage data to demonstrate its practical use. We use the average surplus or deficit and fairness index to measure the system's performance in various scenarios. AdapShare outperforms a quasi-static resource allocation scheme based on long-term network demand statistics, particularly when available resources are scarce or exceed the aggregate demand from the networks. Lastly, we present a high-level O-RAN compatible architecture using RL agents, which demonstrates the seamless integration of AdapShare into real-world deployment scenarios.

翻译：开放无线接入网络（O-RAN）倡议以其开放的接口和具备AI/ML能力的无线接入网络智能控制器（RIC）为特征，促进了无线接入网络之间有效的频谱共享。在此背景下，我们提出AdapShare，这是一种兼容O-RAN的解决方案，它利用强化学习（RL）进行基于意图的频谱管理，其主要目标是最大限度地减少无线接入网络中的资源盈余或赤字。通过部署强化学习智能体，AdapShare能够智能地学习网络需求模式，并利用这些模式来分配资源。我们展示了AdapShare在LTE与NR网络之间频谱共享场景中的有效性，结合了真实世界的LTE资源使用数据和合成的NR使用数据，以证明其实际应用价值。我们使用平均盈余/赤字和公平性指数来衡量系统在各种场景下的性能。AdapShare的表现优于基于长期网络需求统计的准静态资源分配方案，尤其是在可用资源稀缺或超过网络总需求的情况下。最后，我们提出了一种使用强化学习智能体的高层O-RAN兼容架构，该架构展示了AdapShare在实际部署场景中的无缝集成。