臺灣博碩士論文加值系統

正交分頻多工（orthogonal frequency division multiplexing ; OFDM）是一多載波調變技術，以其高頻譜效率而廣為人知，目前此技術已被使用於許多無線通訊系統中。OFDM之所以有高的頻譜使用效率是因為將頻譜切成多個子載波，且子載波間相互重疊且正交。然而當載波頻率偏移(carrier frequency offset; CFO)存在時，正交性會遭到破壞，造成子載波間的相互干擾而降低通訊的品質。因此在真正的數據傳輸前，我們必須先估計並補償CFO。本論文旨在研究OFDM相關系統之CFO最大似然(Maximum likelihood; ML)估計，我們的系統假設在接收端可以接收到未知的週期性訊號。本論文分別就OFDM、正交多頻分工存取(orthogonal frequency division multiple access; OFDMA)上行(uplink)和合作式放大傳輸(amplify-and-forward; AF) OFDM系統，提出其對應之ML解。
在傳統的正交分頻多工系統中，ML解需計算相關矩陣(correlation matrix)的反矩陣，但隨著子載波數的增加，其複雜度將大到無法實現。為避免直接計算該反矩陣，本論文提出一個新的ML法來直接求得CFO的閉合解(closed-form solution)，此法的優點是計算複雜度極低，該ML法亦可延伸至同時估計CFO和符元時間偏移(symbol timing offset)。為評估在CFO的估計效能，我們導出該系統之CFO的理論下限也就是CRB (Cram r rao bound)。本論文接著探討交錯型(interleaved) OFDMA系統上行中的CFO估計問題。由於系統特性使然，此系統接收的訊號一定是週期性的，因此無需額外的訓練訊號。跟OFDM系統一樣我們需計算一相關矩陣的反矩陣，但OFDMA系統因牽涉到多使用者的CFO估計，所以此反矩陣很難計算。我們因此提出使用級數展開法(series expansion) 將此反矩陣展開，並保留適當的低階項後，我們可以得到一個閉合解，然後以解根的方式解得CFO。最後我們推導出相對應的CRB，實驗發現無論是在傳統的OFDM系統或OFDMA系統中，本論文所提的ML解皆可逼近CRB。
最後，本論文探討AF-OFDM系統中的CFO估計和功率分配問題，在此系統中雜訊為有色雜訊(color noise)，然而此特性並未在之前的研究中被討論，因此現有的ML解皆非最佳，現有的CRB也不適用。因為CFO的ML解變的相當複雜無法導出閉合解，本論文提出使用梯度下降法(gradient descent method)來求得CFO的ML解。此外，有色雜訊使得此系統中的CRB過於複雜以致於無法得到一簡單的閉合解。本論文做了一些近似來推導出CRB的閉合解。實驗發現此近似的CRB是準確的且我們所提出的ML解可以逼近此CRB。我們接者提出兩個功率分配法將CRB最小化，並利用梯度下降法求得其解。模擬發現，本論文提出的功率分配法不但可以大幅改善CFO估計的準確性，也可提升了系統的訊雜比(signal-to-noise ratio)。

Orthogonal frequency division multiplexing (OFDM), being a
multicarrier modulation technique, is well known for its high
spectral efficiency and has been adopted in many wireless systems.
The high efficiency of OFDM comes from the fact that the spectrums
of its subcarriers are overlapped and orthogonal each other.
However, when the carrier frequency offset (CFO) is present, the
orthogonality between the subcarriers is lost and inter carrier
interference (ICI) is induced causing performance degradation. As a
result, CFO must be estimated and compensated before the actual
transmission can be conducted. In this dissertation, we study the
maximum-likelihood (ML) methods for CFO estimation in OFDM-based
systems, assuming that unknown periodic received sequences are
available at the receivers. Specifically, we solve the ML CFO
estimation problems in conventional OFDM, orthogonal frequency
division multiple access (OFDMA) uplink, and cooperative
amplify-and-forward (AF) OFDM systems.

In conventional OFDM systems, the ML CFO estimator requires the
inversion of an correlation matrix. When the number of subcarriers
is large, the computational complexity can become prohibitively
high. We then develop a new ML method that can yield a closed-form
solution without the inversion. The advantage of the proposed method
is that the required computational complexity is low. The proposed
method is further extended to a joint CFO and symbol timing offset
(STO) estimation. Theoretical Cram$\acute{e}$r-Rao lower bounds
(CRBs) are also derived to verify the optimality of the proposed
approaches. We then investigate the CFO estimation problem in
interleaved OFDMA uplink systems. Since the periodicity is inherent
in OFDMA systems, no training sequences are required. As previously,
there is an correlation matrix in the likelihood function to be
inverted. Since multi-users are involved, the CFOs in the likelihood
function become intractable after the matrix inversion. We propose
using a series expansion method to express the inverted matrix. By
properly truncating the expansion, we can obtain a closed-form
expression, solve the optimum CFOs with a root-finding method, and
derive the corresponding CRB. Simulations show that the performance
of the proposed method can approach the CRB.

We finally consider the ML CFO estimation and the power allocation
problem in cooperative AF-OFDM systems. In this scenario, the noise
at the destination becomes colored. The colored-noise problem has
not been considered before. Thus, the existing ML methods are not
optimal and the existing CRBs are not valid. Since the likelihood
function is a complicated function of the CFO, we then propose a
gradient-descent method to solve the problem. The expression for the
CRB for the CFO estimation in AF-OFDM systems is even more
complicated and a simple solution cannot be obtained. We then
propose an approximation method such that a closed-form solution can
also be derived. Simulations show that the approximated CRB is
accurate and the performance of the proposed gradient-descent method
can approach the CRB. Minimizing the approximated CRB, we further
propose two power allocation algorithms (PAA), implemented with
constrained gradient-based method, for the source and relays.
Simulations show that not only the performance of the CFO estimation
is greatly enhanced, but also the signal-to-noise ratio (SNR)
between source and destination is improved.

1 Introduction 1
1.1 Conventional and Proposed Methods . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 CFO Estimation in OFDM Systems . . . . . . . . . . . . . . . . . . . 3
1.1.2 CFO Estimation in OFDMA Systems . . . . . . . . . . . . . . . . . . 4
1.1.3 CFO Estimation in AF-OFDM Systems . . . . . . . . . . . . . . . . . 5
1.2 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Maximum Likelihood Timing and Carrier Frequency Offset Estimation for OFDM
Systems with Periodic Preambles 9
2.1 Existing Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Proposed ML CFO Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 12
ix
2.3 Proposed Joint ML STO and CFO Estimation . . . . . . . . . . . . . . . . . . 20
2.4 Performance Analysis of STO Estimation . . . . . . . . . . . . . . . . . . . . 22
2.5 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 Blind Maximum-Likelihood Carrier-Frequency-Offset Estimation for Interleaved
OFDMA Uplink Systems 33
3.1 The Propposed CFO Estimation Method . . . . . . . . . . . . . . . . . . . . . 34
3.1.1 Signal Model for Interleaved OFDMA Uplink System . . . . . . . . . . 34
3.1.2 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.1 Truncation Error in (3.14) . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.2 CRB Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.1 System Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.2 Performance Assessment for ￠q = 2 . . . . . . . . . . . . . . . . . . . 50
3.3.3 Performance Assessment for ￠q = 4 . . . . . . . . . . . . . . . . . . . 51
4 CFO Estimation and Power Allocation in Amplify-and-Forward CooperativeOFDM
Systems 59
4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.1.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.1.2 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2 ML CFO Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 CRB Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.4 Power Allocation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5 Conclusions 89
x
Appendix A Detailed Derivations of (2.19), (2.38), and the Statistics of Ai and »i 93
A.1 Derivation of (2.19) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
A.2 Derivation of (2.38) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
A.3 Derivations of 1i
A, 1i
», oi
A, oi
», and ·i
A» . . . . . . . . . . . . . . . . . . . . . . . 98
Appendix B Detailed Derivations of (3.22) and (3.27) 105
B.1 Derivation of (3.22) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
B.2 Derivation of (3.27) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Appendix C Detailed Derivation of (4.37) 111
C.1 Derivation of (4.37) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Bibliography 113

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