Authors - Felipe M. Coelho, Margarida N. P. dos Santos, Jeziel M. Pessoa, William A. P. de Melo, Joel C. do Nascimento, Carlos A. O. de Freitas , Debora R. Raimundo, Vandermi J. da Silva Abstract - The transition from 4G to 5G networks, particularly in Non Standalone (NSA) deployments, introduces new challenges for the energy effi ciency of mobile devices, as they must maintain simultaneous connectivity with LTE for signaling while using 5G NR for high-speed data transmission. To ad dress this issue, this work proposes a hybrid artificial intelligence approach for predicting current consumption that combines conventional deep learning with neuromorphic computing principles. Real-world telemetry data are first pro cessed using convolutional layers and bidirectional LSTM units to capture spa tial and temporal patterns, and the resulting representations are then converted through rate coding and provided to a Spiking Neural Network (SNN). The model is trained using a hybrid strategy that integrates Spike-Timing Dependent Plasticity (STDP) with surrogate gradients, together with a custom loss function designed to emphasize prediction accuracy during high-demand periods. Experimental results show that the proposed model achieves an RMSE of 0.1164 mA, representing a 6.3% improvement compared to standard Recur rent Spiking Neural Network (RSNN) approaches, indicating its ability to cap ture abrupt variations in power consumption typical of 5G NSA environments.
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