A fluid analysis of utility-based wireless scheduling policies

Peijuan Liu*, Randall Berry, Michael L. Honig

*Corresponding author for this work

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

4 Scopus citations


We consider packet scheduling for the downlink in a wireless network, where each packet's service preferences are captured by a utility function that depends on the packet's delay. The goal is to schedule packet transmissions to maximize the total utility. We examine a simple gradient-based scheduling algorithm, the U̇R-rule, which is a type of generalized cμ-rule (Gcμ) that takes into account both a user's channel condition and derived utility. We study the performance of this scheduling rule for a draining problem. We formulate a "large system" fluid model for this draining problem where the number of packets increases while the packet-size decreases to zero, and give a complete characterization of the behavior of the U̇R scheduling rule in this limiting regime. We then give an optimal control formulation for finding the optimal scheduling policy for the fluid draining model. Using Pontryagin's minimum principle, we show that, when the user rates are chosen from a TDM-type of capacity region, the U̇R rule is in fact optimal in many cases. Finally, we consider non-TDM capacity regions and show that here the U̇R rule is optimal only in special cases.

Original languageEnglish (US)
Title of host publication2004 43rd IEEE Conference on Decision and Control (CDC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0780386825
StatePublished - 2004
Event2004 43rd IEEE Conference on Decision and Control (CDC) - Nassau, Bahamas
Duration: Dec 14 2004Dec 17 2004

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other2004 43rd IEEE Conference on Decision and Control (CDC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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