发表机构
Hanbat National University（韩北国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出MLLM-coRIC框架，利用多模态大语言模型结合自然语言需求和射频上下文，实现RAN智能控制中的自适应联合资源与功率分配，无需重新设计算法。该框架通过分层架构和闭环优化，适应动态网络条件，并在多小区仿真环境中验证其有效性。
AI 中文摘要
基于人工智能（AI）的无线接入网（RAN）控制器通常针对预定义的操作场景和优化任务进行设计，这限制了其在部署后网络条件和运营商需求发生变化时的适应性。本文提出了多模态大语言模型（MLLM）引导的RAN智能控制约束优化（MLLM-coRIC），这是一种需求自适应的分层开放式RAN（O-RAN）框架，用于联合资源与功率分配。MLLM-coRIC将运营商的自然语言规范与射频（RF）衍生的网络上下文作为多模态输入联合利用，使得单个部署的框架能够处理不同的优化问题，而无需重新设计特定任务的算法或重新训练控制策略。在非实时RAN智能控制器（Non-RT RIC）中，MLLM对运营商需求、预测的网络演进和测量的优化结果进行整体性的、较长时间尺度的推理，以设计数值控制损失。通过闭环过程迭代细化相对约束惩罚，使优化准则既能反映预期的网络行为，也能反映预期的运行上下文。在近实时RIC（Near-RT RIC）中，一种损失条件化的混合执行器通过结合学习到的斜坡约束资源分配与基于模型的干扰感知功率控制，将综合准则转化为快速无线电动作。实现了一个集成CARLA城市移动性模拟器和基于Sionna RT射线追踪的无线传播模拟器的多小区评估环境以进行验证。
英文摘要
Artificial intelligence (AI)-based radio access network (RAN) controllers are commonly designed for predefined operating scenarios and optimization tasks, limiting their adaptability when network conditions and operator requirements change after deployment. This paper proposes multimodal large language model (MLLM)-guided constrained optimization for RAN intelligent control (MLLM-coRIC), a requirement-adaptive hierarchical Open RAN (O-RAN) framework for joint resource and power allocation. MLLM-coRIC jointly exploits the operator's natural-language specification and radio-frequency (RF)-derived network context as multimodal inputs, enabling a single deployed framework to address different optimization problems without redesigning task-specific algorithms or retraining control policies. At the Non-Real-Time RAN Intelligent Controller (Non-RT RIC), the MLLM performs holistic, longer-timescale reasoning over the operator requirement, predicted network evolution, and measured optimization outcomes to design the numerical control loss. The relative constraint penalties are iteratively refined through a closed-loop process, allowing the optimization criterion to reflect both the intended network behavior and the expected operating context. At the Near-Real-Time RIC (Near-RT RIC), a loss-conditioned hybrid executor translates the synthesized criterion into fast radio actions by combining learned ramp-constrained resource allocation with model-based interference-aware power control. A multi-cell evaluation environment integrating the CARLA urban mobility simulator and the Sionna RT ray-tracing-based wireless propagation simulator is implemented for validation.
