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研究生: 黃士展
Shih-Chan Huang
論文名稱: 長程演進上鏈通訊單載波分頻多工之通道估測技術
Channel Estimation Techniques of SC-FDM in LTE Uplink Communications
指導教授: 林嘉慶
Jia-Chin Lin
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: 97
語文別: 英文
論文頁數: 81
中文關鍵詞: 功率峰均比頻域最小均方誤差估測頻域最小平方估測塊狀引導信號編排正交分頻多重存取單載波分頻多重存取
外文關鍵詞: PAPR, block-type pilot arrangement, SC-FDMA, FD-LMMSE, FDLS, OFDMA
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  • 近來,由於單載波多重存取(SC-FDMA)技術具有較低的功率峰均比,長程演進(LTE)規格已將單載波分頻多重存取制訂成下一代行動通訊系統上鏈傳輸之多重存取技術。單載波分頻多重存取系統可視為正交分頻多重存取(OFDMA)系統的預先編碼版本,作法是將其資料先行經過離散傅利葉轉換(DFT)編碼。 此預先編碼的動作有助於降低功率峰均比,進而達到節省行動傳輸工具電力。 然而,像正交分頻多重存取系統所遭遇的問題一樣,單載波分頻多重存取對震盪器不穩及通道都卜勒頻移產生的頻率偏移敏感,此現象將造成子載波間干擾而使得系統效能降低。
    為了增進系統效能及降低子載波干擾,通道估測技術是必須的。 實際上,在單載波分頻多重存取系統上用於通道估測的引導信號(pilot signals)是在固定週期時間插入資料符元之間,此種編排技術被稱為塊狀引導信號編排(block-type pilot arrangement)。 因此,基於此種引導信號編排方式,此篇論文提出一種頻域滑窗最小平方(FDLS)估測技術來估測通道變化。此提出的估測技術將與傳統的頻域最小平方估測和頻域最小均方誤差估測技術(FD-LMMSE)在高都卜勒頻移通道下做性能上的比較。 此外,時域估測技術及其時域等化器也會在此篇論文中研究並且與頻域估測技術相互比較。


    Recently, Single Carrier Frequency Division Multiple Access (SC-FDMA) has been considered as a promising uplink transmission scheme for next generation mobile communication in LTE specification due to its low peak to average power ratio (PAPR). SC-FDMA system is a pre-coding scheme of OFDMA system, and its data signals are pre-coded by DFT. The pre-coding operation will reduce the PAPR, and it has benefit for mobile transmitters. However, similar to OFDMA, SC-FDMA is highly sensitive to frequency offsets caused by oscillator inaccuracies and the Doppler shift, which inevitably result in inter-carrier-interference (ICI), and will degrade the system performance.
    In order to enhance system performance and mitigate ICI in SC-FDM, channel estimation techniques are necessary. In practice, the pilot signals for channel estimation in SC-FDMA systems are inserted to all subcarriers periodically in time, which is called block-type pilot arrangement. Therefore, based on the block-type pilot arrangement, we propose a frequency domain least-square (FDLS) with a redundant sliding rectangular window estimator to track the channel variations. The proposed method will be compared with conventional FDLS estimators and frequency-domain linear minimum mean-square-error (FD-LMMSE) estimators in high Doppler spread channels. Also, time domain estimators with associated equalizers are investigated and compared in this thesis.

    Chapter 1 System Description 1 1.1 Introduction to E-UTRA LTE 1 1.2 Downlink Multiple Access Scheme — OFDMA 1 1.2.1 The concept of OFDM 1 1.2.2 Continuous-time and discrete-time model 3 1.3 Uplink Multiple Access Scheme — SC-FDMA 5 1.3.1 The concept of SC-FDM 5 1.3.2 The advantages of SC-FDMA 6 1.4 Long-Term Evolution Frame and Slot Structure 9 1.4.1 Frame Structure type 1 and type 2 9 1.4.2 Slot structure and resource grid 10 Chapter 2 Channel Characteristics 13 2.1 Introduction to mobile radio propagation 13 2.2 Large-scale fading 14 2.2.1 Log-distance path loss model 15 2.2.2 Log-normal shadowing model 16 2.3 Small-scale fading 17 2.3.1 Complex baseband multipath channel model 18 2.3.2 Channel autocorrelation function 19 2.3.3 Coherence bandwidth of the channel 20 2.3.4 Coherence time of the channel 22 2.3.5 Discrete-time discrete-delay channel model 23 2.3.6 Categories of small-scale fading 23 2.4 Rayleigh fading channel model 25 2.4.1 Baseband channel mathematical models 25 2.4.2 Simulations for Jakes’model 27 Chapter 3 Characterization of Pilot Sequences 31 3.1 Pilot sequences analysis - time domain approach 31 3.1.1 LSSE criterion for channel estimation 31 3.1.2 Mean squared channel estimation error 33 3.1.3 Quality measure of channel estimation and upper bound 34 3.2 Pilot sequences analysis - frequency domain approach 35 3.2.1 GLF criterion for channel estimation 35 3.3 Signal ambiguity functions 37 3.3.1 Properties of the ambiguity function 38 3.3.2 Basic radar signals 39 3.3.3 Phase-coded pulse 41 Chapter 4 Channel Estimation Techniques for LTE Uplink Systems 47 4.1 Pilot signal arrangement 47 4.1.1 Block-type pilot arrangement 47 4.1.2 Comb-type pilot arrangement 47 4.2 Reference signal in LTE uplink systems 49 4.3 Pilot subchannel estimation techniques 50 4.3.1 Frequency domain least-squares (LS) pilot channel estimation 51 4.3.2 Frequency domain linear minimum mean-square error (LMMSE) pilot channel estimation 53 4.3.3 Frequency domain least-squares windowing pilot channel estimation 55 4.3.4 Time domain least-squares pilot channel estimation and zero-forcing equalizer 56 4.4 Data-block channel estimation techniques 59 4.4.1 Block data subchannel estimation 59 4.4.2 Decision-directed data subchannel estimation 60 4.4.3 Linear-interpolation data subchannel estimation 61 4.5 Simulation 62 4.5.1 Channel model and simulation parameters 62 4.5.2 Comparative simulations 65 Chapter 5 Conclusion 77 Bibliography 78

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