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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.13675 |
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| _version_ | 1866916295441645568 |
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| author | Villena-Rodríguez, Alejandro Gómez, Gerardo Aguayo-Torres, Mari Carmen Martín-Vega, Francisco J. Outes-Carnero, José Ng-Molina, F. Yak Ramiro-Moreno, Juan |
| author_facet | Villena-Rodríguez, Alejandro Gómez, Gerardo Aguayo-Torres, Mari Carmen Martín-Vega, Francisco J. Outes-Carnero, José Ng-Molina, F. Yak Ramiro-Moreno, Juan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR Villena-Rodríguez, Alejandro Gómez, Gerardo Aguayo-Torres, Mari Carmen Martín-Vega, Francisco J. Outes-Carnero, José Ng-Molina, F. Yak Ramiro-Moreno, Juan Information Theory 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. |
| title | AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR |
| topic | Information Theory |
| url | https://arxiv.org/abs/2406.13675 |