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Learning radio resource management in RANs: Framework, opportunities, and challenges

Learning radio resource management in RANs: Framework, opportunities, and challenges
Learning radio resource management in RANs: Framework, opportunities, and challenges

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.

0163-6804
138-145
Calabrese, Francesco Davide
c66ca4d8-4caf-411d-85a8-45cdd887d658
Wang, Li
cee5932f-1921-4acd-a835-404eb0a5853d
Ghadimi, Euhanna
27ba210d-b1d8-430d-bb69-a170b1610f22
Peters, Gunnar
02307514-59d7-4c24-a3e2-cfc28f6de310
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Soldati, Pablo
a31fb87e-eb62-40dd-864b-71512b7da0f0
Calabrese, Francesco Davide
c66ca4d8-4caf-411d-85a8-45cdd887d658
Wang, Li
cee5932f-1921-4acd-a835-404eb0a5853d
Ghadimi, Euhanna
27ba210d-b1d8-430d-bb69-a170b1610f22
Peters, Gunnar
02307514-59d7-4c24-a3e2-cfc28f6de310
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Soldati, Pablo
a31fb87e-eb62-40dd-864b-71512b7da0f0

Calabrese, Francesco Davide, Wang, Li, Ghadimi, Euhanna, Peters, Gunnar, Hanzo, Lajos and Soldati, Pablo (2018) Learning radio resource management in RANs: Framework, opportunities, and challenges. IEEE Communications Magazine, 56 (9), 138-145. (doi:10.1109/MCOM.2018.1701031).

Record type: Article

Abstract

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.

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More information

e-pub ahead of print date: 17 September 2018
Published date: September 2018

Identifiers

Local EPrints ID: 426907
URI: https://eprints.soton.ac.uk/id/eprint/426907
ISSN: 0163-6804
PURE UUID: 075cada4-5ff9-43b4-9a33-3c78273b6eae
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 14 Dec 2018 17:30
Last modified: 14 Mar 2019 01:55

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Contributors

Author: Francesco Davide Calabrese
Author: Li Wang
Author: Euhanna Ghadimi
Author: Gunnar Peters
Author: Lajos Hanzo ORCID iD
Author: Pablo Soldati

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