5524826
doi
10.5281/zenodo.5524826
oai:zenodo.org:5524826
user-h2020daemon
user-eu
Giulia Attanasio
IMDEA Networks Institute
Marco Fiore
IMDEA Networks Institute
Joerg Widmer
IMDEA Networks Institute
Traffic-Driven Sounding Reference Signal Resource Allocation in (Beyond) 5G Networks
Claudio Fiandrino
IMDEA Networks Institute
doi:10.1109/SECON52354.2021.9491611
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Beyond 5G mobile networks have to support a wide range of performance requirements and unprecedented levels of flexibility. To this end, massive MIMO is a critical technology to improve spectral efficiency and thus scale up network capacity, by increasing the number of antenna elements. This also increases the overhead of Channel State Information (CSI) estimation and obtaining accurate CSI is a fundamental problem in massive MIMO systems. In this paper, we focus on scheduling uplink Sounding Reference Signals (SRSs) that carry pilot symbols for CSI estimation. Under the large number of users and high load that are expected to characterize beyond 5G systems, the limited amount of resources available for SRSs makes the legacy 3GPP periodic allocation scheme largely inefficient. We design TRADER, an SRS resource allocation framework that minimizes the age of channel estimates by taking advantage of machine learning-based short-Term traffic forecasts at the base station level. By anticipating traffic bursts, TRADER schedules SRS resources so as to obtain CSI for each user right before the corresponding traffic arrives. Experiments with extensive real-world mobile network traces show that our solution is efficient and robust in high load scenarios: with respect to a round robin schedule of aperiodic SRS, TRADER provides more often CSI within the coherence time (up to 5× for given scenarios), leading to channel gains of up to 2 dB.</p>
This work is partially supported by the Juan de la Cierva grant from the Spanish Ministry of Science and Innovation (IJC2019-039885-I), by the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 "DAEMON," and by the Madrid Regional Government through the TAPIR-CM program (S2018/TCS- 4496).
Zenodo
2021-09-23
info:eu-repo/semantics/preprint
5524825
user-h2020daemon
user-eu
1
award_title=Network intelligence for aDAptive and sElf-Learning MObile Networks; award_number=101017109; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/101017109; funder_id=00k4n6c32; funder_name=European Commission;
1632491304.381022
1907459
md5:0a170c1c536d7f441dbb552e30332427
https://zenodo.org/records/5524826/files/datadriven-srs_pp.pdf
public
10.1109/SECON52354.2021.9491611
Is previous version of
doi
10.5281/zenodo.5524825
isVersionOf
doi