Proc. IEEE International
Conference on Acoustics, Speech, and Signal Processing,
April 16-20, Honolulu, HI USA,
accepted for publication.
Optimal OFDMA Resource Allocation with
Linear Complexity to Maximize Ergodic Weighted Sum Capacity
Ian C. Wong and
Brian L. Evans
Department of Electrical
and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712 USA
Allocation Results by Prof. Evans' Group
from Dr. Ian Wong's PhD Dissertation
Previous research efforts to optimize OFDMA resource allocation
with respect to communication performance have focused on formulations
considering only instantaneous per-symbol rate maximization,
and on solutions using suboptimal heuristic algorithms.
This paper intends to fill gaps in the literature through two key
First, we formulate weighted sum ergodic capacity maximization
in OFDMA assuming the availability of perfect channel
state information (CSI).
Our formulations exploit time, frequency, and multi-user diversity,
while enforcing various notions of fairness through weighting factors
for each user.
Second, we derive algorithms based on a dual optimization framework
that solve the OFDMA ergodic capacity maximization problem with
O(M K) complexity per OFDMA symbol for
M users and K subcarriers, while achieving data
rates shown to be at least 99.9999% of the optimal rate in simulations
based on realistic parameters.
Hence, this paper attempts to demonstrate that OFDMA resource allocation
problems are not computationally prohibitive to solve optimally, even when
considering ergodic rates.
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Last Updated 12/12/14.