Problem
Research questions and friction points this paper is trying to address.
O-RAN
slicing xApps
resource allocation
service-level agreements
deep reinforcement learning
Evolving Inspectable O-RAN Slicing xApps with LLMs
🤖 AI Summary
该研究使用大型语言模型生成可读且可编辑的Python程序来优化O-RAN切片控制器，以适应动态变化并满足服务等级协议。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Evolving Slicing Controllers
Large Language Models (LLMs)
Inspectable and Editable Logic
Adaptive Resource Allocation
Open RAN (O-RAN)
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