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-ave...
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| creator | Villena-Rodríguez, Alejandro Gómez, Gerardo Aguayo-Torres, Mari Carmen Martín-Vega, Francisco J Outes-Carnero, José Ng-Molina, F. Yak Ramiro-Moreno, Juan |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2406.13675 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2406.13675</identifier><language>eng</language><subject>Computer Science - Information Theory ; Mathematics - Information Theory</subject><creationdate>2024-06</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2406.13675$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2406.13675$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Villena-Rodríguez, Alejandro</creatorcontrib><creatorcontrib>Gómez, Gerardo</creatorcontrib><creatorcontrib>Aguayo-Torres, Mari Carmen</creatorcontrib><creatorcontrib>Martín-Vega, Francisco J</creatorcontrib><creatorcontrib>Outes-Carnero, José</creatorcontrib><creatorcontrib>Ng-Molina, F. Yak</creatorcontrib><creatorcontrib>Ramiro-Moreno, Juan</creatorcontrib><title>AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR</title><description>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
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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.</abstract><doi>10.48550/arxiv.2406.13675</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Information Theory Mathematics - Information Theory |
| title | AI-Assisted Dynamic Port and Waveform Switching for Enhancing UL Coverage in 5G NR |
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