Gomes, Nathan;
Chughtai, Mohsan;
Assimakopoulos, Philippos;
(2024)
Reinforcement Learning-Assisted Transmit Signal Power Savings in Variable Bit-rate Fronthaul.
IEEE Communications Letters
, 28
(6)
pp. 1313-1316.
10.1109/LCOMM.2024.3386848.
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Abstract
The increasing bit-rate demands placed on the fronthaul from higher user rates and multiple antenna technologies will make the consideration of its power consumption an important issue. In this study, it is assumed that the fronthaul bit-rate can be reduced from the maximum required rate through prediction of the fronthaul traffic using deep reinforcement learning (DRL). Using such predictions, and benchmarked simulations of a discrete multitone (DMT) modulation electro-absorption modulator (EAM)-based optical fiber-link, as an example of a fronthaul transmission system, it is shown that the power reduction from reducing the transmitter signal power alongside the reduction in modulation level can be between 22.3% and 34.6% within a fixed bandwidth of 34 GHz and 18 GHz respectively. Such a transmitter could be built as a bandwidth variable transponder in a Flexible Ethernet fronthaul.
| Type: | Article |
|---|---|
| Title: | Reinforcement Learning-Assisted Transmit Signal Power Savings in Variable Bit-rate Fronthaul |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/LCOMM.2024.3386848 |
| Publisher version: | https://doi.org/10.1109/LCOMM.2024.3386848 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Mobile fronthaul, beyond-5G, discrete multitone, modulation, power conservation. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10190405 |
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