发表机构
Oakland University; Princeton University; Ghent University–imec（奥克兰大学; 普林斯顿大学; 根特大学-艾迈克）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NOEMA是一个开源工具包，通过可执行合约确保学习型无线组件在仿真、训练和基准测试中的比较条件一致，并提供可复用的开发与验证路径。
AI 中文摘要
基于学习的无线研究日益将仿真、外部模型训练和基准测试相结合。为每个实验重建这些阶段耗时费力，而各阶段之间的差异可能悄然改变比较的条件。我们提出了NOEMA，一个开源工具包，它从共享的、机器可检查的无线场景出发，准备模型开发和基线评估。从同一场景出发，NOEMA可以准备基准执行、捕获对齐的训练数据，并导出用于模型开发的可微组件。因此，研究人员可以专注于训练和选择他们的模型，然后将选定的组件返回到相同的受控实验中进行评估。NOEMA检查返回的模型，并验证声明的比较条件（包括可用信息、随机条件和失败核算）在基准测试和报告过程中保持一致。一个包含的智能体控制示例将这些合约应用于一个语言模型监督器，该监督器根据历史链路反馈选择分配策略和功率预算。工作流检查的所有43个回归测试均产生了预期结果，包括拒绝所有31个包含已知不一致性的案例。单独的保真度测试表明，捕获的数据和返回的模型行为得以保留。在一项20次重复的正交相移键控相位跟踪研究中，学习接收机与五导频平滑之间的平均误码率差异为-0.01526（95%置信区间：-0.01547至-0.01506）。因此，NOEMA既为开发学习型无线组件提供了一条可复用的路径，也提供了一种保持最终比较可检查和可验证的方式。
英文摘要
Learning-based wireless research increasingly combines simulation, external model training, and benchmarking. Rebuilding these stages for every experiment is time-consuming, while differences introduced between them can silently change the conditions of a comparison. We present NOEMA, an open-source toolkit that prepares model development and baseline evaluation from a shared, machine-checkable wireless scenario. From the same scenario, NOEMA can prepare benchmark execution, capture aligned training data, and export differentiable components for model development. Researchers can therefore focus on training and selecting their models, then return the selected component to the same controlled experiment for evaluation. NOEMA checks the returned model and verifies that declared comparison conditions, including available information, random conditions, and failure accounting, remain consistent through benchmarking and reporting. An included agentic-control example applies these contracts to a language-model supervisor that selects allocation policies and power budgets from historical link feedback. All 43 regression tests of the workflow checks produced their expected outcomes, including rejection of all 31 cases containing known inconsistencies. Separate fidelity tests showed that captured data and returned-model behavior were preserved. In a 20-replicate quadrature phase-shift keying phase-tracking study, the mean bit-error-rate difference between the learned receiver and five- pilot smoothing was -0.01526 (95% confidence interval: -0.01547 to -0.01506). NOEMA therefore provides both a reusable path for developing learned wireless components and a way to keep their final comparisons inspectable and checkable.
