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Opportunistic Scheduling with Deadline Constraints in Wireless Networks

  • David I Shuman
  • Mingyan Liu
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 46)

Abstract

The main idea of opportunistic scheduling in wireless networks is to exploit the temporal and/or spatial variations of the wireless channel by transmitting more data when the channel between the sender and receiver is in a “good” state, and less data when the channel is in a “bad” state. Doing so increases system throughput and reduces total energy consumption. However, in such opportunistic transmission scheduling problems, it is often the case that the transmission scheduler has competing Quality of Service (QoS) interests. In this chapter, we focus on delay-sensitive applications. For example, in a multimedia streaming application, if packets do not arrive before a certain deadline, the end user may experience playout interruptions or poor playout quality. After a brief introduction to opportunistic scheduling in wireless networks, we examine stochastic modeling issues common to such applications, such as ways to model the wireless channel, the data, the system performance objectives, and the system resource constraints. We then review the formulations of a few key stochastic optimization problems featuring deadline constraints, and present the structures of their optimal transmission scheduling policies. We elucidate the role of the deadline constraints by comparing these problems in dierent ways. Finally, we relate these wireless communication problems to models from inventory theory.

Keywords

Channel Condition Channel State Information Deadline Constraint Dynamic Programming Equation Opportunistic Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Electrical Engineering and Computer Science DepartmentUniversity of MichiganAnn ArborUSA

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