Convergence and optimality of channel aware scheduling and resource allocation algorithms<br/><br/>

Wednesday, June 29, 2005 - 1:30pm - 2:30pm
EE/CS 3-180
Rajeev Agrawal (Motorola)
With an abstraction of serving rate-adaptive sources on a broadcast-type
wireless channel as a utility maximization problem, it is shown how one
can design many intuitive online scheduling policies based upon the feedback
that one obtains at the scheduler. Using a stochastic approximation argument
it is then shown that the constructed algorithms converge to optimal solutions
of the utility maximization problem over different sets which critically
depend on the quality of the feedback information.

We then apply the theory developed above to the downlink in a CDMA based
wireless network. In terms of operational variables the problem is to select
a subset of the users for transmission at each transmission oppurtunity and
for each of the users selected, to choose the modulation and coding scheme,
transmission power, and number of codes used. We refer to this combination as
the physical layer operating point (PLOP). Each PLOP consumes different amounts
of code and power resources. Thus, the task is to pick the optimal PLOP
taking into account both system-wide and individual user resource constraints
that can arise in a practical system. Using an information theoretic model
for the achievable rate per code results in a tractable convex optimization problem.
By exploiting the structure of this problem, we give algorithms for finding
the optimal solution with geometric convergence. We also use insights obtained
from the optimal solution to construct low complexity near optimal algorithms
that are easily implementable. Numerical results comparing these algorithms are
also given.