Reimagining Wirless Connectivity Beyond 5G

In this paper, a universal multi-modal feature embedding and dimension reduction scheme was presented for improving prediction and intelligence performance of future 6G communication systems. By integrating heterogeneous features—including THz-band measurements, mmWave characteristics, AI-based network features and evolving temporal factors—our model provide an effective representation of complex dependencies in the emerging 6G. The feature extraction pipeline, assisted with spectral, statistical and dynamic profiling promises a rich representation of network behavior. Experimental results show that our proposed embedding and ensemble-based prediction model achieves consistent improvement over unimodal, traditional fusion methods for both classification and regression tasks. With the lowest RMSE and MAE values, and the highest accuracy, precision, recall, F1score, AUC value as well as MCC and R² (R-squared) coefficients of determination under various 6G environments during dense device density scenarios, mobility and different type of THz propagation features. The findings confirm that multimodal fusion combined with dimensionality reduction and deep learning-based optimization provides an effective and scalable solution for intelligent 6G network prediction and management.  The framework not only enhances predictive performance but also contributes to the development of autonomous and self-optimizing communication systems required in future    6G networks.  

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