AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR
<|reference_start|>AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR: The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM), which is appealing for cell-edge users using high-frequency bands, since it shows a smaller peak-to-average power ratio, and allows a higher transmit power. Nevertheless, DFT-S-OFDM exhibits a higher block error rate (BLER) which complicates an optimal waveform selection. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL). In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users' service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. Results show that our proposed scheme greatly outperforms both metrics compared to classical approaches.<|reference_end|>
@article{villena-rodríguez2024ai-assisted,
    title={AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL
  Coverage in 5G NR},
    author={Alejandro Villena-Rodr'iguez, Gerardo G'omez, Mari Carmen
  Aguayo-Torres, Francisco J. Mart'in-Vega, Jos'e Outes-Carnero, F. Yak
  Ng-Molina, Juan Ramiro-Moreno},
    journal={arXiv preprint arXiv:2406.13675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13675},
    primaryClass={cs.IT math.IT}
}