发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用LLM将O-RAN切片控制器演化为可读可编辑的Python程序，在5G测试平台上提升44.5%吞吐量，并将SLA违例率从79.9%降至2.2%。
AI 中文摘要
开放无线接入网（O-RAN）切片xApps必须适应变化的信道条件和流量需求，同时满足服务级别协议（SLA）。深度强化学习可以产生自适应策略，但其分配规则仍编码在神经网络参数中。我们的目标是在保持这种适应性的同时，使控制器的决策逻辑对运营商直接可检查和可编辑。我们使用大型语言模型（LLM）将切片控制器演化为紧凑的Python程序，其决策逻辑在优化后仍保持可读和可编辑。LLM离线提出并修订候选方案，而校准的模拟器对其进行评分，选定的决策模块在O-RAN控制路径中原样运行。在NSF POWDER 5G测试平台上，演化出的控制器从保证切片中释放资源，该切片的吞吐量目标在持续信道衰落下达不到，将尽力而为吞吐量从158.2 Mbps提高到228.6 Mbps，相比最佳静态分配提升了44.5%。由于控制器是可读的源代码，其行为可从其方程预测，缺陷可通过阅读代码诊断，校准错误可通过单行修改纠正，在一个案例中将SLA违例从79.9%降至2.2%，在另一个案例中使适应度提高一倍以上。在针对同一测试平台校准的四切片轨迹驱动模拟中，在匹配的提案预算下，演化搜索比独立提示获得更高的平均评估分数，在保留轨迹上的平均归一化增益分别为：仅提示16.3%，从头演化32.1%，从起始程序演化51.0%。
英文摘要
Open RAN (O-RAN) slicing xApps must adapt resource allocations to changing channel conditions and traffic demands while meeting service-level agreements (SLAs). Deep reinforcement learning can produce adaptive policies, but their allocation rules remain encoded in neural-network parameters. Our goal is to retain this adaptability while making the controller's decision logic directly inspectable and editable by operators. We use a large language model (LLM) to evolve slicing controllers as compact Python programs whose decision logic remains readable and editable after optimization. The LLM proposes and revises candidates offline, while a calibrated simulator scores them, and the selected decision module runs unchanged in the O-RAN control path. On the NSF POWDER 5G testbed, the evolved controller releases resources from a guaranteed slice whose throughput target becomes unattainable under a sustained channel fade, improving best-effort throughput from 158.2 to 228.6 Mbps, a 44.5% gain over the best static allocation. Since the controllers are readable source code, their behavior can be predicted from their equations, defects can be diagnosed by reading the code, and calibration errors can be corrected with one-line edits, reducing SLA misses from 79.9% to 2.2% in one case and more than doubling fitness in another. In a four-slice trace-driven simulation calibrated to the same testbed, evolutionary search achieves higher average evaluation scores than independent prompting at a matched proposal budget, with mean normalized gains on held-out traces of 16.3% for prompting alone, 32.1% for evolution from scratch, and 51.0% for evolution from a starting program.
