臺灣博碩士論文加值系統

本論文首先針對無線通訊系統建立一個一般型式的時空訊號模型。然後基於這個時空訊號模型發展一個低複雜性、高準確度的階層式時-空拆解技術的多重參數演算法。該階層式時-空拆解技術結合了估測法與濾波器，以建立一個階層式的樹狀結構，交替估測時間及空間上的參數。在空間與時間估測器之間，由前一級估測的參數所建立的濾波器來對入射信號進行分群，進而加強估測的準確性。因此本論文所提出之方法可解析出在空間中或在時域中非常靠近之信號。另外在階層式的樹狀結構估測過程中，時間及空間上的參數可自動的配對，並無須再另外使用配對演算法。

接著我們利用階層式時-空拆解技術發展一個在無線通訊系統中同時估測方位角及載波頻率的多重信號分辨演算法，稱為頻-時-頻多重信號分辨演算法。該演算法結合了時域濾除及空間之波束形成技術配合二次頻-多重信號分辨演算法及一次時-多重信號分辨演算法將被接收信號分群,隔離,進而估測及配對被接收信號之方位角及載波頻率。此外為了得到更強健的時域濾除及空間之波束形成技術，本論文提出由線性條件式最小雜訊準則所決定的限制型時域濾除及限制型空間之波束形成技術。同時一種低複雜度的估測法-以平方根的空/時間濾波器在本論文中亦被提出，以降低整體的計算量。

最後，本論文將階層式時-空拆解技術推廣到分碼多工和跳頻系統上做方位角及延遲時間的估測。對於分碼多工系統而言，本論文先對接收訊號做拆解，使其能適用於階層式樹狀結構演算法。至於跳頻系統，由於它的時間特徵函數是一個對角矩陣，因此無法形成時域濾波器。本論文提出利用堆疊技巧及奇異值分解的方法來解決這個問題。

In this dissertation, a general space-time signal model is first built up and then based on which a low complexity, yet high accuracy hierarchical space-time decomposition (HSTD) technique is developed to jointly estimate the temporal and spatial parameters. The HSTD technique, which is a novel twist of parameter estimation and filtering processes, employs lower dimensional subspace-based algorithms - in a hierarchical tree structure - to estimate the temporal and spatial parameters alternatively. In between every other subspace-based algorithm, a temporal filtering process or a spatial beamforming process, implemented based on the previous estimated parameters, is invoked to partition the incoming rays into appropriate groups and to annihilate the additive noise to enhance the estimation accuracy, so that the incoming rays can be well resolved even with very close temporal parameters or spatial parameters. In addition, since the HSTD technique proceeds the parameter estimation in the tree structure, the estimated spatio-temporal parameters are automatically paired off.

Then, based on the HSTD technique, a frequency-space-frequency MUltiple SIgnal
Classification (FSF MUSIC) algorithm is presented to estimate the DOAs and
carrier frequencies of the impinging rays, in which two one-dimensional (1-D) frequency (F)-MUSIC and one 1-D space (S)-MUSIC along with filering/beamforming
processes are employed to estimate the DOA and frequencies alternatively in a coarsefine manner. Some implementation related issues such as robustness and parsimonious computations are also addressed. Statistical analyses of the undesired residues propagating between the MUSICs and the mean square errors (MSEs) of the parameter estimates, based on an illustrative three-ray scenario, are derived to provide further insights into the proposed approach.

Also, the HSTD technique is applied to jointly estimate the DOAs and path delays in CDMA and FH systems. For CDMA systems, the received data are first analyzed and then a hierarchically tree-structured algorithm is developed to jointly estimate these two parameters. For FH systems, two algorithms are proposed, both of which begin with the estimation of the DOAs, following by a spatial beamforming process and the delay estimation by utilizing the filtered data in each hop. Thereafter, after partitioning the data through a temporal filtering process, two new approaches are addressed to achieve finer estimates of the DOAs.

Simulations are also provided to verify the proposed algorithms and the developed analytic expressions in various scenarios. As a whole, these new algorithms strike a good balance between performance and complexity as compared with previous works.

1 INTRODUCTION 1
1.1 Review of Previous Works . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 ML Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Subspace-Based Algorithm . . . . . . . . . . . . . . . . . . . . . .5
1.2 Overview of the Dissertation . . . . . . . . . . . . . . . . . . . . . 8
1.2.1 Rationale of Hierarchical Space-Time Decomposition . . . . . . .....9
1.2.2 Outline of the Dissertation . . . . . . . . . . . . . . . . . . . 11
2 SPACE-TIME CHANNELS AND PARAMETRIC DATA MODELS ...........................14
2.1 The Wireless Communication Channels . . . . . . . . . . . . . . . . . 14
2.1.1 Multiplicative Noise . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.2 Data Model in Temporal Domain . . . . . . . . . . . .... . . . . . 17
2.1.3 Antenna Array . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.2 Space-Time Signal Model . . . . . . . . . . . . . . . . . . . . . .. . 21
2.2.1 System Model for DOA and Carrier Frequency Estimation in General
Communication Systems . . . . . . . . . . . . . . . . ............ 23
2.2.2 System Model for DOA and Delay Estimation in DS/ CDMA Systems . . 24
2.2.3 System Model for DOA and Delay Estimation in FH Systems .......... 26
2.2.4 System Model for Azimuth, Elevation, and Frequency Estimation .....27
2.2.5 System Model for DOA and Frequency Offset Estimation in Uplink
OFDM Networks . . . . . . . . . . . . . . . . . . . . . ...... . . 28
2.3 Hierarchical Space-Time Decomposition . . . . . . . . . . . . . . . . 29
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3 JOINT ANGLE AND CARRIER FREQUENCY ESTIMATION I: ALGORITHM, ROBUSTNESS
AND COMPLEXITY REDUCTION .................................................34
3.1 The FSF MUSIC Algorithm . . . . . . . . . . . .. . . . . . . . . . . . 35
3.2 Robustness Consideration . . . . . . . . . . . . . . . . . . . . . . . 44
3.3 Reduction of Computational Complexity . . . .. . . . . . . . . . . . . 51
3.4 SIMULATIONS AND DISCUSSION . . . . . . ....... . . . . . . . . . . . . 55
3.5 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . 62
4 JOINT ANGLE AND CARRIER FREQUENCY ESTIMATION II: PERFORMANCE ANALYSIS ....64
4.1 MSE of the Rough Frequency Estimates . . . . . . . . . . . . . . . . . 66
4.2 Power of Residue Rays due to Imperfect Temporal Filtering . . .... . . 68
4.3 Perturbation of Eigenvectors . . . . . . . . . . . . . . . . . . . . . 71
4.4 MSE of the DOA Estimates . . . . . . . . . . . . . . . . . . . . . . . 73
4.5 MSE of the Frequency Estimates . . . . . . . . . . . . . . . . . . . . 76
4.6 SIMULATIONS AND DISCUSSION . . . . . . . . . . . . . . ....... . . . . 84
4.7 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 95
5 JOINT ANGLE AND DELAY ESTIMATION 96
5.1 Joint Angle and Delay Estimation in CDMA Systems . . . . . . ... . . . 97
5.1.1 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.1.2 The Proposed New TST MUSIC . . . . . . . . . ........ . . . . . . . 99
5.1.3 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . 103
5.2 Joint Angle and Delay Estimation in FH Systems . . . . . . . . . . . 107
5.2.1 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . 118
5.3 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6 CONCLUSIONS .............................................................124
6.1 Summary of the Dissertation . . . . . . . . . . . . . . . . . . . . . 124
6.2 Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . 126
REFERENCE .................................................................128
A Relationship Between the Temporal Vector and Covariance Matrix after
Filtering Processes .....................................................137
B Proof of (3.55) .........................................................139
C Proof of (3.35) .........................................................141
D Proof of (4.13) and (4.14) ..............................................143
E The Taylor Expansion of the Perturbed Eigenvectors in (4.21) ............146
F Proof of (4.34) .........................................................148

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