事后共形预测用于可靠无线通信

中文摘要 AI 辅助

在自动驾驶等高风险无线应用中部署人工智能（AI）需要保证可靠运行。这种保证通常可以通过设计系统来实现，这些系统基于一组预测，采用保守策略以适应该集合内所有可能的结果。例如，在基于位置的波束选择中，基站可以识别一组可能的位置，并选择一个确保在该集合上具有高容量的波束。该集合相对于真实结果（例如，真实用户位置）的误覆盖概率则量化了中断概率，而集合的大小决定了最终性能或资源预算（例如，传输容量）。传统共形预测（CP）适用于目标误覆盖水平预先规定的情况。然而，在实际无线系统中，预测集合可能是在规定的操作约束下选择的，因此需要事后量化由此产生的误覆盖概率。本文开发了一个正式的统计框架，用于数据驱动的预测集合选择，该框架能够对由此产生的误覆盖概率提供可靠的估计。该方法建立在向后共形预测和可能近似正确共形预测的基础上，为误覆盖概率提供了无分布假设的可靠性保证。我们将该框架应用于三个不同的无线应用：窄带干扰检测、近场定位和基于码本的波束识别。数值结果验证了可靠性保证，并表明所提出的事后共形方法在所有考虑的应用中都能达到与朴素基于概率的方法相当的准确性。

英文摘要

Deploying artificial intelligence (AI) in high-stakes wireless applications such as autonomous transportation requires guarantees of reliable operation. Such guarantees can often be obtained by designing systems that act on a set of predictions via conservative policies catering to all possible outcomes within the set. For instance, in location-based beam selection, a base station may identify a set of plausible locations and select a beam that ensures high capacity over the set. The miscoverage probability of the set with respect to the true outcome (e.g., the true user location) then quantifies the outage probability, while the size of the set determines the final performance or resource budget (e.g., the transmission capacity). Conventional conformal prediction (CP) applies when the target miscoverage level is prescribed in advance. However, in practical wireless systems, prediction sets may instead be selected under prescribed operational constraints, requiring the resulting miscoverage probability to be quantified post hoc. This paper develops a formal statistical framework for the data-driven selection of prediction sets that provides a reliable estimate of the resulting miscoverage probability. The methodology builds on backward CP and probably approximately correct CP, yielding distribution-free reliability guarantees on the miscoverage probability. We apply the framework on three distinct wireless applications: narrowband interference detection, near-field localization, and codebook-based beam identification. Numerical results validate the reliability guarantees and show that the proposed post-hoc conformal methods achieve accuracy comparable to the naive probability-based approach across all considered applications.

发表机构

• KTH Royal Institute of Technology（皇家理工学院）
• King’s College London（伦敦国王学院）
• Institute for Intelligent Networked Systems, Northeastern University London（东北大学伦敦智能互联系统研究所）

机构由 AI 辅助整理，请以论文原文为准。