IEEE Transactions on Wireless Communications,
vol. 7, no. 3, Mar. 2008, pp. 962-971.
Optimal Downlink OFDMA Resource Allocation with
Linear Complexity to Maximize Ergodic 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 78712-1084 USA
OFDMA Resource Allocation Results by Prof. Evans' Group
from Dr. Ian Wong's PhD Dissertation
OFDMA resource allocation assigns subcarriers and power, and possibly
data rate, to each user.
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 continuous and discrete ergodic weighted sum rate
maximization in OFDMA assuming the availability of perfect channel state
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 rate 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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