What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems

2024-09-30

arXiv.org

AI Summary

Modern wireless networks such as O-RAN face a fundamental challenge in counterfactual KPI analysis—namely, estimating how key performance indicators (KPIs) would change under alternative RAN applications, given that such counterfactual outcomes are unobservable in practice. Method: This paper introduces conformal prediction to wireless-system counterfactual KPI analysis for the first time, integrating causal inference with O-RAN application-layer abstraction to construct a statistically rigorous, individually calibrated counterfactual estimation framework. The approach mitigates covariate shift between offline log data and online deployment and supports validation across MAC- and PHY-layer applications. Results: Experiments demonstrate that the proposed method achieves guaranteed coverage of KPI counterfactual error bounds at the prescribed confidence level—significantly outperforming conventional regression and propensity score matching. It thus provides a trustworthy, quantifiable tool for post-hoc network attribution analysis and policy optimization.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In modern wireless network architectures, such as Open Radio Access Network (O-RAN), the operation of the radio access network (RAN) is managed by applications, or apps for short, deployed at intelligent controllers. These apps are selected from a given catalog based on current contextual information. For instance, a scheduling app may be selected on the basis of current traffic and network conditions. Once an app is chosen and run, it is no longer possible to directly test the key performance indicators (KPIs) that would have been obtained with another app. In other words, we can never simultaneously observe both the actual KPI, obtained by the selected app, and the counterfactual KPI, which would have been attained with another app, for the same network condition, making individual-level counterfactual KPIs analysis particularly challenging. This what-if analysis, however, would be valuable to monitor and optimize the network operation, e.g., to identify suboptimal app selection strategies. This paper addresses the problem of estimating the values of KPIs that would have been obtained if a different app had been implemented by the RAN. To this end, we propose a conformal-prediction-based counterfactual analysis method for wireless systems that provides reliable error bars for the estimated KPIs, despite the inherent covariate shift between logged and test data. Experimental results for medium access control-layer apps and for physical-layer apps demonstrate the merits of the proposed method.

Problem

• Wireless Networks
• Key Performance Indicators (KPIs)
• Network Optimization

Innovation

• Counterfactual Analysis
• KPI Prediction
• Wireless Network Optimization