Proc. 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 -

Paper - Slides

OFDMA Resource Allocation Results by Prof. Evans' Group

Software Release 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 algorithms. This paper intends to fill gaps in the literature through two key contributions. 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.