Learn-and-adapt network resource allocation

Tianyi Chen, Qing Ling, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the empirical optimal Lagrange multiplier from historical data, and adapt to the upcoming resource allocation strategy. Remarkably, it only requires one more sample (gradient) evaluation than the celebrated stochastic dual gradient (SDG) method. LA-SDG can be interpreted as a foresighted learning approach with an eye on the future. It is established - both theoretically and empirically - that LA-SDG markedly improves the cost-delay tradeoff over state-of-the-art network resource allocation schemes.

Original languageEnglish (US)
Title of host publication18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781509030088
DOIs
StatePublished - Dec 19 2017
Event18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017 - Sapporo, Japan
Duration: Jul 3 2017Jul 6 2017

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2017-July

Other

Other18th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2017
Country/TerritoryJapan
CitySapporo
Period7/3/177/6/17

Bibliographical note

Funding Information:
Work in this paper was supported by NSF 1509040, 1508993, 1509005, NSF China 61573331, NSF Anhui 1608085QF130, and CAS-XDA06011203

Funding Information:
Work in this paper was supported by NSF 1509040, 1508993, 1509005, NSF China 61573331, NSF Anhui 1608085QF130, and CAS-XDA06011203.

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