RT Journal Article
T1 AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR
A1 Villena-Rodríguez, Alejandro
A1 Martín-Vega, Francisco-Javier
A1 Gómez-Paredes, Gerardo
A1 Aguayo-Torres, María del Carmen
A1 Outes-Carnero, José
A1 Ng-Molina, Francisco Yak
A1 Ramiro-Moreno, Juan
K1 Aprendizaje automático (Inteligencia artificial)
K1 Sistemas de comunicación inalámbricos
AB 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) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM leads to higher throughput when the PAs are operating in their linear region, which is mostly the case for cell-interior users, whereas DFT-S-OFDM is more appealing when PAs are exhibiting non-linear behavior, which is associated with cell-edge users. Therefore, existing waveform selection solutions rely on predefined signal-to-noise ratio (SNR) thresholds that are computed offline. However, the varying user and channel dynamics, as well as their interactions with power control, require an adaptable threshold selection mechanism. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions. 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. In addition, the solution accounts for the switching cost, which is related to the interruption of the communication after every switch due to implementation issues, which has not been considered in existing solutions. Results show that our proposed scheme achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput.
PB MDPI
YR 2025
FD 2025-09-19
LK https://hdl.handle.net/10630/40850
UL https://hdl.handle.net/10630/40850
LA eng
NO Villena-Rodríguez, A.; Martín-Vega, F.J.; Gómez, G.; Aguayo-Torres, M.C.; Outes-Carnero, J.; Ng-Molina, F.Y.; Ramiro-Moreno, J. AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR. Sensors 2025, 25, 5875. https://doi.org/10.3390/s25185875
NO Ministerio de Ciencia, Innovación y Universidades
NO Agencia Estatal de Investigación
NO Unión Europea
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026