PR<sup>3</sup>: Power Efficient and Low Latency Baseband Processing for LTE Femtocells

Budhdev, Nishant; Chan, Mun Choon; Mitra, Tulika

doi:10.1109/infocom.2018.8486276

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…Our first finding is that the power consumption associated with the BBU processing is comparable to the RF chain's transmission power. This result is consistent with previous studies; for example, [55] estimated that 40% of a femtocell's power consumption is due to its BBU. In detail, Fig.…”

Section: Preliminary Experimental Analysissupporting

confidence: 93%

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Ayala‐Romero

García‐Saavedra²,

Costa‐Pérez

et al. 2023

IEEE Trans. on Mobile Comput.

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Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power consumption profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.

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Section: Preliminary Experimental Analysissupporting

confidence: 93%

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Ayala‐Romero

García‐Saavedra²,

Costa‐Pérez

et al. 2023

IEEE Trans. on Mobile Comput.

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Energy-Constrained Online Matching for Satellite-Terrestrial Integrated Networks

Wang

Gao

Huang

et al. 2021

ICC 2021 - IEEE International Conference on Communications

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Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

Gao

Wang

Huang

et al. 2023

IEEE Trans. on Mobile Comput.

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PR³: Power Efficient and Low Latency Baseband Processing for LTE Femtocells

Cited by 6 publications

References 16 publications

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Energy-Constrained Online Matching for Satellite-Terrestrial Integrated Networks

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

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PR3: Power Efficient and Low Latency Baseband Processing for LTE Femtocells

Cited by 6 publications

References 16 publications

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Energy-Constrained Online Matching for Satellite-Terrestrial Integrated Networks

Energy-Constrained Online Scheduling for Satellite-Terrestrial Integrated Networks

Contact Info

Product

Resources

About

PR³: Power Efficient and Low Latency Baseband Processing for LTE Femtocells