This work presents a two-stage ordinal optimization theory-based approach for solving the throughput maximization problems with power constraints of sub-carrier assignment and power allocation in multi-user orthogonal frequency division multiplexing uplink systems. In the first stage, a crude but efficient model is employed to evaluate the performance of a sub-carrier assignment pattern and the genetic algorithm is used to search through the huge solution space. In the second stage, an exact model is employed to evaluate s best sub-carrier assignment patterns obtained in stage 1 and form the select subset. Finally, the best one of the select subset is the good enough solution that we seek. Via numerous tests, this work demonstrates the efficiency of the proposed algorithm and compares it with those of other heuristic methods.

In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.