 |
In Tab. I, we present the comprehensive results of train
mean squared error (MSE) loss, number of parameters, test
MSE and test mean absolute error (MAE) for our proposed
method compared to the five baselines. From the table we find
among all the models, the BiLSTM achieves the best overall
performance, recording the lowest train MSE (0.0001), lowest
test MSE (0.10), and lowest test MAE (0.05). While the CNN
offers the smallest parameter footprint (8628), it lags behind
BiLSTM in terms of predictive accuracy. Although models
like LSTM and Transformer exhibit strong individual traits
such as the Transformer¡¯s relatively low test MSE (0.07) they
either suffer from higher MAE or require significantly more
parameters. The BiLSTM strikes the optimal balance between
accuracy and model complexity, making it the most suitable
choice for the prediction-aware traffic steering framework in
O-RAN scenarios. |
IEEE/ICACT20260367 Slide.17
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