IEEE Global Communications Conference,
November 16-20, Washington, DC USA,
accepted for publication.
Optimal Downlink OFDMA Subcarrier, Rate, and Power Allocation
with Linear Complexity to Maximize Ergodic Weighted-Sum Rates
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 on OFDMA subcarrier, rate, and power
allocation have focused on formulations considering only instantaneous
per-symbol rate maximization, and on solutions using suboptimal heuristic
This paper intends to fill gaps in the literature through two
First, we consider ergodic weighted sum rate maximization in OFDMA,
allowing us to exploit the temporal dimension, in addition to
the frequency and multi-user dimensions available in
instantaneous rate optimization, while enforcing various
notions of fairness through weighting factors for each user.
Second, we derive algorithms based on a dual optimization framework
that solves the 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.