In this chapter, we have proposed a computationally efficient three-stage OO approach to solve the large-dimension ASABA problem of multiuser OFDM system for a good enough
solution. By looking into the insight of the ASABA problem (2.3), we reformulate it into (4.4) and develop an approximate objective function as well as a subtle representation scheme and a repair operator for the GA employed in Stage 1, which makes our OO approach possible in handling the huge discrete solution space as well as the constraints. The easily computed surrogate models employed in Stages 1 and 2 help resolve the computation complexity caused by the hard-to-evaluate objective function. These factors contribute most to the computational efficiency of our algorithm. Furthermore, we have demonstrated the superiority of our algorithm by comparing with four existing algorithms through numerous test cases in the aspects of solution quality and computational efficiency. More importantly, our approach has wide range of applications in resource allocation problems of wireless network and communication.
Chapter 5
Conclusions and Future Work 5.1 Conclusions
Two multi-stage OO based methods to solve the ASABA problem of the multiuser OFDM system for a good enough solution have been presented and discussed. The first method can meet the real-time application requirement while with the assistance of hardware. The second method is computationally efficient for solving the large dimension ASABA problem of multiuser OFDM system.
The first method presented in Chapter 3 consists of four OO stages to find a good enough solution to the ASABA problem. In the first three stages, we use surrogate models to quickly evaluate the estimated performance of a solution so as to select an estimated good enough subset from the candidate solution set using limited computation time. When the size of the solution space is huge, the reduction of the search space can be done in several stages. The surrogate models in the stages can range from very rough to more refined ones, and the exact model will be employed in the last stage when there are only few solutions left in the candidate solution set. The four-stage OO approach ensures the quality of the obtained solution, however at the cost of solving a continuous version of the considered problem in the first stage. To resolve this computational complexity problem, we propose a hardware implementable DPG method to exploit deep submicron technology so as to obtain the optimal continuous solution extremely fast.
Due to the large dimension of the ASABA problem, implementing the first stage in hardware is almost impossible for area concern. Therefore in the first stage of our second method presented in Chapter 4, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to efficiently search through the huge solution space to find I (=200) good solutions.
Numerical results and comparisons with various existing algorithms are provided to demonstrate the potential of our proposed techniques. It is shown that the proposed resource allocation methods substantially improve the system power efficiencies. In the meantime, the
proposed resource allocation algorithms are more computationally efficient. Moreover, the first method can meet the real-time application requirement and the second method is suitable for large dimensional ASABA problems.
5.2 Future Work
The proposed algorithms are based on the assumption that perfect channel information is available for adaptive resource allocation. In practice, the estimated channel information may be not very accurate either because of the estimation error or because of the delay between the estimation and the transmission instances. It is therefore worth studying adaptive resource allocation schemes while considering channel mismatch.
The circuit of the proposed hardware architecture is based on equations (2.1) and (2.3), however, it is not general enough to hold for all practical systems. For example, the required power fk(c) in 2
,
) (
n k k c f
α may correspond to certain coding and modulation schemes. Thus, we need to modify hardware circuit of the DPG method for other specific fk(c). Because changing hardware is not as easy as the software, it is therefore worth studying the easily implemented hardware architecture for different communication system to meet real-time application requirement.
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List of Publication 著作目錄 姓名：黃榮壽 (Jung-Shou Huang)
期刊論文著作：
1. Shin-Yeu Lin and Jung-Shou Huang, “Adaptive Subcarrier Assignment and Bit Allocation for Multiuser OFDM System Using Ordinal Optimization Approach,” IEEE Transactions on Vehicular Technology, vol. 57, no. 5, pp.
2907-2919, Sep. 2008. (EI, SCI)
2. Shin-Yeu Lin and Jung-Shou Huang, “A Computationally Efficient Method for Large Dimension Subcarrier Assignment and Bit Allocation Problem of Multiuser OFDM System,” accepted to appear in IEICE Transactions on Communications. (EI, SCI)
研討會論文著作：
1.林心宇、黃榮壽，“一個用於多重使用者正交分頻多工系統中有效率的次
載波指定與位元分配方法”，2007 年資訊科技應用學術研討會，新竹，八
月三十一日，2007。
博士候選人學經歷資料
姓名：黃榮壽 性別：男
生日：中華民國59 年 3 月 30 日 籍貫：台灣省台中縣
論文題目：中文：以序的最佳化理論為基礎的多重使用者正交分頻多工系統的適應性次
載波指定與位元分配方法
英文：Adaptive Subcarrier Assignment and Bit Allocation Methods for Multiuser OFDM System Using Ordinal Optimization Approach
學歷：
1. 私立淡江大學電機工程學系學士，民國 83 年 9 月~85 年 6 月
2. 國立交通大學電機與控制工程學系碩士，民國 85 年 9 月~87 年 6 月 3. 國立交通大學電機與控制工程學系博士班，民國 91 年 9 月~97 年 9 月
經歷：
1. 泰威科技電子工程師，民國 87 年~88 年 2. 世紀民生設計工程師，民國 88 年~89 年 3. 義隆電子設計工程師，民國 89 年~97 年
Vita
Jung-Shou Huang was born in Taiwan, R.O.C.. He received the B.S. degree in electrical engineering from Tamkang University in 1996 and the M.S. degree in electrical and control engineering from National Chiao Tung University in 1998. He has been studying for his Ph. D.
degree in electrical and control engineering from National Chiao Tung University since September 2002.
Currently, he also works as a digital IC designer in Elan Microelectronics Corporation. His major research interests include optimization theory with applications, image processing and digital IC design.