## 3 The Proposed FED-XAI Framework for QoE Predictions in V2X Environments
### Details of the Proposed FED-XAI Framework
The results of the prediction feed a dashboard that displays them in real time and explains how they were obtained. The above scenario will be evaluated in a real-time distributed testbed, which embodies both the communication and computation aspects of the system, as well as the application logic. The communication is realized by Simu5G, a modular simulator of 3 GPP-compliant New Radio based on OMNeT++ [22], which also works in real time and interfaces with external applications [23]. The MEC subsystem is realized using Intel's OpenNESS open-source framework (www.openness.org (accessed on 16 August 2022)). Moreover, QoS information is taken in real time from Simu5G, also through its MEC service interface, that can be queried by MEC applications. In order to make the aforementioned testbed more realistic, the network scenario implemented by Simu5G is designed considering data taken from TIM's live network as input, such as base stations position and user data volume, extracted using the techniques described in Section 3.3. We have recently carried out a preliminary experimental analysis focused on QoE forecasting in B5G/6G networks [24]: we have presented a novel data set (QoE forecasting data set: [[http://www.iet.unipi.it/g.nardini/ai6g_qoe_dataset.html](http://www.iet.unipi.it/g.nardini/ai6g_qoe_dataset.html)]([http://www.iet.unipi.it/g.nardini/ai6g_qoe_dataset.html](http://www.iet.unipi.it/g.nardini/ai6g_qoe_dataset.html)) (accessed on 16 August 2022)) obtained through realistic network simulations and showed how decision trees as an inherently explainable model can be considered a valid baseline for the prediction task. Specifically, the data set consists of time-tagged contextual (e.g., UE position), QoS (e.g., Signal to Interference plus Noise Ratio (SINR) value measured at packet reception) and QoE (e.g., percentage of a frame arrived at the time of its display) metrics from 24 repetitions of a scenario in which 15 instances of UE experiment with a video for approximately 120 s. The prediction task has been formulated as a regression problem. The preprocessing and feature extraction steps are extensively described in [24]; in a nutshell, for each UE we collected the timeseries related to 12 metrics (QoS, QoE and contextual) and obtained any record of the preprocessed data set as follows: for a timestamp \(t\), the input variables consist of 11 statistics (i.e., mean, median, max, min, variance, standard deviation, kurtosis, skewness, Q1 and Q3, number of samples) measured for each metric in the time window \([t-W,t]\) (with \(W=10\) s), whereas the output variable consists in the mean of the target QoE metric over the time horizon of one second (i.e., in \([t,t+H]\), with \(H=1\) s). For the preliminary experimental analysis, we considered the _centralized_ setting, i.e., all data available on a single node, and resorted to the decision tree for regression available in scikit-learn ([[https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html)) (accessed on 16 August 2022)). Figure 4 reports an example of real and predicted timeseries for a given QoE metric. Although our final goal is to learn XAI models in a _federated_ fashion, such a preliminary analysis allowed us to set a baseline for the _centralized_ setting and to assess the performance of an XAI model in a prediction task on realistic B5G/6G network simulations. Figure 3: QoE prediction based on previous QoS/QoE samples.