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研究生： 王易凡
Yi-Fan Wang
論文名稱： 適用於多輸入多輸出天線架構下向上鏈結正交分頻多重接取系統之盲目載波頻率偏移估測
Blind Carrier Frequency Offset Estimation in OFDMA Uplink with MIMO Structure
指導教授： 陳永芳
Yung-Fang Chen
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 通訊工程學系
Department of Communication Engineering
畢業學年度： 95
語文別： 英文
論文頁數： 67
中文關鍵詞： 載波頻率偏移 、正交分頻多重接取系統 、遞迴方法 、馬可夫鏈蒙地卡羅
外文關鍵詞： Markov chain Monte Carlo, iteration method, OFDMA, Carrier frequency offset
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    • 在正交分頻多重接取系統中盲目式載波頻率偏移估測演算法已經在近年來發展於一些文獻中。其中在文獻[1]中提出的盲目式估測方法受到很大的關注，此方法使用多重信號特徵(MUSIC)的演算法在運算上需要大量的搜尋使得運算複雜度提高，且此法也有估測失誤的問題。因此在本篇論文中我們達成了下面兩項任務。首先，我們提出了一個簡易的遞迴方法使得運算複雜度能夠降低，模擬的結果也驗證了這方法的效用且估測結果接近克拉美-羅邊界(CRB)。其次，我們利用貝式(Bayesian)方法透過馬可夫鏈蒙地卡羅 (MCMC)的方式來減輕估測失誤的問題，模擬結果也顯示其擁有優異的效果。此外我們提出的方法適用於多輸入多輸出(MIMO) 天線架構，在此種架構下能提高載波頻率偏移的估測結果。

    Blind carrier frequency offset (CFO) estimation algorithms in orthogonal frequency division multiple access (OFDMA) systems have been developed in literatures recently. In [1], the blind estimation scheme has drawn a lot attention, but the computational complexity caused by the MUSIC exhaustive search is high and the scheme has the mismatch problem. Therefore, in this thesis, we have two works as follows. First, a simple iteration scheme is proposed to reduce the computational complexity. The simulation results demonstrate the efficacy of the proposed scheme and the performance is near to the Cramer-Rao bound (CRB). Secondly, we utilize the Markov chain Monte Carlo (MCMC) method based on a Bayesian approach as the estimator in order to mitigate the mismatch problem. Simulation results show that the proposed method gains the superior performance. Besides, the proposed algorithms are capable of the multiple antennas with MIMO structure which enhances the performance of CFO estimation.

    Contents I List of Figures III List of Tables VI Chapter 1 1 Introduction 1 Chapter 2 System Model 4 2.1 Interleaved Subcarrier Assignment Scheme 4 2.2 Interleaved OFDMA Uplink System Model 5 2.3 Forming to DOA Model 9 2.4 With MIMO Structure 11 Chapter 3 Simple Iteration Method 12 3.1 MUSIC-based Approach 12 3.2 Maximum Likelihood and Approximate Maximum Likelihood Approach 14 3.3 Proposed Iteration Method 16 3.4 CRB of the CFO Estimation 19 Chapter 4 Markov Chain Monte Carlo Method 22 4.1 Bayesian Approach 23 4.2 MCMC Algorithm 25 4.3 Modified Posterior Function 26 Chapter 5 Simulation Results 30 5.1 Simulation Parameters 30 5.2 Simulation Results of Simple Iteration Method 31 5.2.1 MSE and RMSE versus SNR 31 5.2.2 Performance comparison under different number of users 34 5.2.3 Performance comparison with MIMO structure 38 5.3 Simulation Results of MCMC Method 41 5.3.1 MSE and RMSE versus SNR 41 5.3.2 Performance comparison under different number of users 44 5.3.3 Performance comparison with MIMO structure 46 5.3.4 Mismatch probability 49 Chapter 6 Conclusions 51 Reference 52 Appendix A 54

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