A Power Consumption Model and Energy Saving Techniques for 5G-Advanced Base Stations
Aiming at minimizing the base station (BS) energy consumption under low and medium load scenarios, the 3GPP recently completed a Release 18 study on energy saving techniques for 5G NR BSs [1]. A broad range of techniques was evaluated in terms of the obtained network energy saving (NES) gain and the...
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Published in | 2023 IEEE International Conference on Communications Workshops (ICC Workshops) pp. 605 - 610 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
28.05.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Aiming at minimizing the base station (BS) energy consumption under low and medium load scenarios, the 3GPP recently completed a Release 18 study on energy saving techniques for 5G NR BSs [1]. A broad range of techniques was evaluated in terms of the obtained network energy saving (NES) gain and their impact to the user-perceived throughput (UPT). In this paper, we present the 3GPP BS power consumption model defined for such evaluation and we provide an overview of promising NES techniques studied by 3GPP. We then proceed to a detailed performance evaluation for a selection of them, focusing on, e.g., the achieved NES gain and the average UPT loss. In detail, we describe time domain adaptation techniques for the scarcer transmission of synchronisation system blocks (SSB) and system information blocks (SIB1), as well as the clustered paging transmissions and the cell discontinuous transmission. We extracted results for the SSB/SIB1 transmission adaptation techniques, showing that a NES gain of 32% to 39% can be achieved. We also described and investigated the performance of spatial and power domain adaptation techniques, where a BS operates with reduced number of antennas and/or transmission power, and we obtained a NES gain up to 40%. The average UPT loss that results as a trade-off for such an NES gain is at least 2%, depending on the studied technique and the cell load. |
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ISSN: | 2694-2941 |
DOI: | 10.1109/ICCWorkshops57953.2023.10283643 |