Mahdi Lotfinezhad,Ben Liang, and Elvino S' Sousa - PowerPoint PPT Presentation

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Mahdi Lotfinezhad,Ben Liang, and Elvino S' Sousa

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Scheduling for Time Varying Channels. Throughput Optimal Policies ... that linear-complexity algorithms are sufficient for non-time varying channels. ... – PowerPoint PPT presentation

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Title: Mahdi Lotfinezhad,Ben Liang, and Elvino S' Sousa


1
On the Stability Region of Linear-MemorySchedu
ling for Time Varying Channels
  • Mahdi Lotfinezhad,Ben Liang, and Elvino S. Sousa
  • ECE Department
  • University of Toronto
  • Summer 2007

2
Throughput Optimal Policies
  • Ensure network stability for any input rate that
    is within the network layer capacity region.
  • One example At each timeslot find a rate vector
    that maximizes backlog-rate product Tassiulas
    Ephremides, INFOCOM92, Neely et al., JSAC05.
  • X(t) Backlog vector, s(t) Channel state
    vector, D(s(t),I) Rate vector, the set of
    schedules.
  • These policies, however, are complex.

3
Related Research
  • Tassiulas INFOCOM98 proved that
    linear-complexity algorithms are sufficient for
    non-time varying channels.
  • We can still use linear-complexity policies for
    time-varying channels Eryilmaz et al ToN05,
    Chaporkar et al INFOCOM06.
  • Problem Ensuring network stability, for all
    input rates within the network layer capacity
    region, requires a memory that exponentially
    increases with the number of users.
  • Question How much sub-optimality in the
    network throughput is introduced by the reduced
    memory requirement?

4
Linear-Memory Policy
The policy generates a candidate schedule
that satisfies the following w.p.
Different values of and allow us to model
algorithms with different complexity levels.
5
Linear-Memory Policy (contd.)
where
6
Rate Region Characterization

7
A Simple Example
A network with two channel states, and three
schedules. We assume that the candidate schedule
is selected from the schedules with equal
probability.
8
Capacity Region Scaling Factor
9
Conclusions
  • We have modeled a class of scheduling policies
    with different complexity levels, and with
    linear-memory requirement.
  • Our analysis has characterized the capacity
    region of these policies, and quantified the
    scaling factor of the stability region based on
    the limiting behavior of rate changes due to
    channel variations and the inefficiencies
    inherent in the scheduling policies
  • Our results indicate how channel memory helps
    reduce the uncertainty of the scheduling policy
    in selecting a suitable candidate schedule
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