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A Computationally Efficient Method for Large Dimension Subcarrier Assignment and Bit Allocation Problem of Multiuser OFDM System
ShinYeu LIN JungShou HUANG
Publication
IEICE TRANSACTIONS on Communications
Vol.E91B
No.12
pp.39663973 Publication Date: 2008/12/01
Online ISSN: 17451345
DOI: 10.1093/ietcom/e91b.12.3966
Print ISSN: 09168516 Type of Manuscript: PAPER Category: Wireless Communication Technologies Keyword: OFDM system, combinatorial optimization, ordinal optimization, resource allocation, wireless communication,
Full Text: PDF(310KB)>>
Summary:
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 offline 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 largedimension 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.

