直接序列展頻通訊系統之低耗能適應性偽雜訊碼擷取架構

Abstract

本論文探討一些先進的方法來提昇直接序列展頻通訊系統之效能，尤其是有關偽雜訊碼設計以及偽雜訊碼擷取系統設計。作者提出了三種不同方法來提昇偽雜訊碼擷取系統之效能。功率消耗、信號頻譜寬度、系統複雜度以及平均擷取時間皆在此論文中作分析討論。為了作分析及理論證實，作者使用ADS做了不同衰減頻道下之系統模擬並製作了晶片作驗證。 第一章做了此篇論文之簡介。第二章探討直接序列展頻通訊系統的定義、組成成分、應用以及製作注意事項。第三章中簡介了直接序列展頻通訊系統中的偽雜訊碼擷取系統。作者討論了有關偽雜訊碼擷取系統的系統效能。三種提昇系統效能的方法也在此做了簡介。第四章探討設計偽雜訊碼擷取系統中偽雜訊碼來提昇系統效能的方法。作者所提出的低功率偽雜訊碼擁有低功率，有效頻寬及較佳系統效能之特性。此章中同時探討了有效率的搜尋演算法來尋找低功率偽雜訊碼。當設計偽雜訊碼擷取系統時，偵測器中之偵測臨界值對系統效能有很大的影響。第五章所提出來的最佳化偵測臨界值演算法可以解得臨界值之最佳解。使用最佳化偵測臨界值演算法，可以將偽雜訊碼擷取系統之偵測機率最大化，而且同時將誤報機率設定在預知的常數。因為在衰減頻道中，頻道雜訊是隨著時間變化的，因此作者在第六章提出一個新的適應性偽雜訊碼擷取系統。這個系統會根據預估通道雜訊值，動態地調整取樣頻率及偵測臨界值。如此一來，此系統可以同時達到最低偵測機率及最高誤報機率之系統規格。此章節所提出之適應性取樣頻率及偵測臨界值控制演算法，搭配第五章的最佳化偵測臨界值演算法，可以設計出最佳化的取樣頻率及偵測臨界值。這個新提出的架構，比起傳統架構省了百分之六十至七十的功率消耗而且可以完全符合系統要求。第七章中，作者做了結論並且討論了未來可以繼續進行之研究方向。

The thesis discusses some advanced approaches to improve the direct-sequence spread-spectrum communication systems, especially on the pseudonoise ( PN ) code design and the PN code acquisition system. Three different approaches to improve performance of PN code acquisition systems are proposed for the use of spread-spectrum wireless applications in this thesis. The power consumption, bandwidth occupation, system complexity, and average acquisition time are comprehensively analyzed when discussing the performance of PN code acquisition systems. System simulation with ADS for fading channels and silicon verification had all been explored for analysis and theoretic proven. Introduction of this thesis in disclosed in Chapter 1. In Chapter 2, the author gives a brief introduction to the spread-spectrum communication systems. The definition of the spread-spectrum communication is given at first and then followed by the components, application, and implementation issues of the direct-sequence spread-spectrum communication systems. The receiver structure of the direct-sequence spread-spectrum communication systems is briefly introduced in Chapter 3. The PN code acquisition system of the direct-sequence spread-spectrum communication system is introduced in this chapter. The system performance issues of the PN code acquisition architecture are discussed in this chapter. The average acquisition time, channel bandwidth, system complexity, and power consumption are considered. The three different approaches to improve the system performance of the PN code acquisition system are also briefly introduced in this chapter. The author proposes an efficient searching algorithm in Chapter 4 to design the efficient PN code applied in the direct-sequence spread-spectrum communication system. The orthogonal degree and toggle rate of the PN code are verified for system performance, bandwidth occupation, and power consumption. The proposed power-saving pseudonoise ( PSPN ) code have the properties with low-power, bandwidth-efficiency, and better system performance when compared to the conventional PN codes. Theoretic analysis of PSPN code is applied at first and then followed by system simulation by ADS. The power consumption is verified by silicon measurement. The author proposes the optimized threshold decision ( OTD ) algorithm in Chapter 5. This algorithm is proposed to design the optimized PN code acquisition system. The optimized PN code acquisition system is defined as the PN code acquisition system with maximal probability of detection and constant probability of false alarm or with minimal probability of false alarm and constant probability of detection. By analysis and simulation, the proposed algorithm is proved to be efficient and convergent guaranteed. A novel low-power, adaptive PN code acquisition architecture is proposed in Chapter 6. The proposed PN code acquisition architecture dynamically updates the sampling rate and decision threshold values according to the estimated channel noise level. The adaptation scheme is implemented by look-up table in power management module, where the values in the look-up table is pre-calculated by the proposed adaptive sampling rate and threshold control ( ASTC ) algorithm. The PN code acquisition architecture is designed to meet the system specifications of minimal probability of detection and maximal probability of false alarm. Combined with the OTD algorithm proposed in Chapter 5, the proposed adaptive sampling rate and threshold control ( ASTC ) algorithm achieves the optimized sampling rate and threshold values for PN code acquisition system. The proposed PN code acquisition system outperforms the conventional PN code acquisition system at power consumption by 60% to 70% and meets the system requirements. Some conclusions and future works are discussed in Chapter 7.