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

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

URL: http://arxiv.org/abs/2410.00150v1
Date: Mon, 30 Sep 2024 18:47:26 GMT
Title: What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems
Authors: Qiushuo Hou, Sangwoo Park, Matteo Zecchin, Yunlong Cai, Guanding Yu, Osvaldo Simeone,
Abstract summary: This paper addresses the "what-if" problem of estimating the values of key performance indicators (KPIs) that would have been obtained if a different app had been implemented by the radio access network (RAN) We propose a conformal-prediction-based counterfactual analysis method for wireless systems that provides reliable "error bars" for the estimated, containing the true with a user-defined probability.
Score: 52.499838151272016
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 performance that would have been obtained with another app. This test, however, would be potentially valuable to monitor and optimize the network operation. With this goal in mind, this paper addresses the "what-if" problem of estimating the values of key performance indicators (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, containing the true KPIs with a user-defined probability, 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.

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