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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2011.8
- 수록면
- 965 - 980 (16page)
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초록· 키워드
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In this paper, the use of conditional Fuzzy C-Means (CFCM) aimed at estimation of desired states of an unknown digital communication channel is investigated for blind channel equalization. In the proposed CFCM, a collection of clustered centers is treated as a set of pre-defined desired channel states, and used to extract channel output states. By considering the combinations of the extracted channel output states, all possible sets of desired channel states are constructed. The set of desired states characterized by the maximal value of the Bayesian fitness function is subsequently selected for the next fuzzy clustering epoch. This modification of CFCM makes it possible to search for the optimal desired channel states of an unknown channel. Finally, given the desired channel states, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In a series of simulations, binary signals are generated at random with Gaussian noise, and both linear and nonlinear channels are evaluated. The experimental studies demonstrate that the performance (being expressed in terms of accuracy and speed) of the Pro-posed CFCM is superior to the performance of the existing method exploiting the "conventional" Fuzzy C-Means (FCM).
목차
ABSTRACT
1. INTRODUCTION
2. OPTIMAL BAYESIAN SOLUTION FOR CHANNEL EQUALIZATION
3. DESIRED CHANNEL STATE CONSTRUCTED BY CHANNEL OUTPUT STATE
4. CONDITIONAL FUZZY CLUSTERING FOR OPTIMAL CHANNEL STATES
5. EXPERIMENTAL RESULTS AND PERFORMANCE EVALUATION
6. CONCLUSIONS
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2013-004-000785949