固定式寬頻無線存取系統之基於適應性類神經模糊推論系統的功率控制

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姓名

李智新(Ze-Shin Lee) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

固定式寬頻無線存取系統之基於適應性類神經模糊推論系統的功率控制
(ANFIS-based Power Control for Fixed Broadband Wireless Access Systems)

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摘要(中)

論文提要內容：
本研究介紹基於模糊控制(Fuzzy control)和適應性類神經模糊推論系統(ANFIS)的功率控制在固定式寬頻無線存取 (Fixed Broadband Wireless Access; FBWA) 系統的應用。區域多點分配服務 (LMDS)是固定式寬頻無線存取系統的代表性系統之一，本論文將模糊控制及結合類神經網路與模糊推論系統之適應性類神經模糊推論系統引進下鏈式功率控制，以期提供基於分碼多工存取(CDMA-based)的LMDS系統一個可以強健、有效的在晴天和雨天操作的功率控制方法。LMDS系統的操作頻率在10GHz以上的頻帶，雨衰減及同頻干擾對LMDS系統通訊品質影響很大。傳統控制系統的設計必需依據明確的數學模型來描述受控系統，對於龐雜、非線性且具有不確定性的受控系統，數學模型不但難以取得且取得的模型與真實情況往往有難以估計的誤差。若考慮的變數愈多、所需處理的問題愈複雜，則建構一個精確的受控系統的模型就愈艱困也愈容易失真。所以，在複雜環境下使用傳統控制方法往往會降低受控系統的整體效率及造成系統的不穩定性。模糊控制具有強健(robustness)、容錯(fault tolerance)及適應(adaptability)等特性、類神經系統則具有自我適應(self-adaptive)、自我學習(self-learning)及抗雜訊(anti-noise)的特性，而兩者優點與缺點具有互補的特性；適應性類神經模糊推論系統具有推論能力，可以處理高階非線性的問題，在各領域的應用已廣為大眾所接受。在LMDS系統中，雨衰減及同頻干擾等通道環境影響因素的影響模型是非線性且較難以精確的數學模型表達。因此，我們提出以模糊邏輯控制和適應性類神經模糊推論系統為基礎的兩種功率控制方案，將通道環境影響因素透過模糊邏輯控制和適應性類神經模糊推論系統來估算通道品質，並以通道品質來調整功率控制的範圍進行衰減補償，進而提昇LMDS的系統性能。模擬結果顯示，我們所提出的兩種功率控制方法，都可以提供LMDS系統一個具有強健性且有更高的頻寬效益之功率控制。

摘要(英)

Abstract
The research in this dissertation introduces fuzzy-based and ANFIS-based downlink power control schemes into fixed broadband wireless access (FBWA) systems to provide the system a robust and efficient operation on both clear sky and rainy conditions. Local multipoint distribution services (LMDS) system is a representative of FBWA, operating at millimeter-wave frequencies above 10 GHz, which offers abundant available bandwidth access to multimedia service for the subscriber without demanding the extending of coaxial cable or fiber to the subscriber plant. Intercell interference and rain attenuation are the major factors limiting capacity in LMDS systems and the impacts of these factors on LMDS system are vague, uncertain, and hard to give a crisp mathematical definition. Fuzzy logic control with the advantages of robustness, fault tolerance, and adaptability can deal with the uncertain problem. Neural network has the significant self-adapting, self-learning and anti-noise characteristics, in which a desired input-output mapping can be obtained by learning a lot of training data. Moreover, the ANFIS system integrates neural network into fuzzy inference system can automatically learn a proper network structure and a set of parameters, simultaneously. Hence, we propose fuzzy-based and ANFIS-based power control schemes, in which fuzzy logic control and ANFIS are employed to evaluate the channel quality by using the environment factors to be as the input variables, and then channel quality is applied to adjust the power control region. Presented and analyzed are fuzzy-based and ANFIS-based downlink power control schemes to show that the proposed power control schemes are appropriate for improving the performance of CDMA-based LMDS systems and can provide the system a robust and efficient operation both on clear sky and rainy conditions.

關鍵字(中)

★ 寬頻無線存取系統
★ 適應性類神經模糊推論系統
★ 功率控制

關鍵字(英)

★ Broadband Wireless Access Systems
★ ANFIS
★ Power Control

論文目次

論文提要內容 i
Abstract iii
誌謝 iv
CONTENTS v
Figure List vii
Table List ix
Chapter 1 Introduction 1
1.1 Background 4
1.2 Motivation 10
1.3 Contributions of Dissertation 11
1.4 Organization 12
Chapter 2 Broadband Wireless Access Systems 13
2.1 Standards and Specifications for Broadband Wireless Access 14
2.2 Radio Propagation 16
2.2.1 Path loss 16
2.2.2 Interference 17
2.2.3 Rain attenuation 19
2.2.4 Cross-polarization discrimination (XPD) 21
2.3 Overview of LMDS 22
2.4 Multiple Access Technologies in LMDS Systems 24
2.4.1 TDMA-based LMDS systems 25
2.4.2 CDMA-based LMDS systems 33
2.5 Capacity Enhancement Methods 37
2.5.1 Frequency reuse 38
2.5.2 Modulation 38
2.5.3 Power control 39
Chapter 3 Performance Analysis of LMDS Systems 45
3.1 System Model 45
3.2 The Definitions of Performance Criteria 50
3.3 Performance Analysis 51
3.3.1 The effects of the TS antenna beamwidth 52
3.3.2 The effects of number of users in each sector 55
3.3.3 The effects of rain rate 58
3.4 Summary 61
Chapter 4 Adaptive Neuro-Fuzzy Inference System 63
4.1 Fuzzy Logic Model 64
4.1.1 The concept of fuzzy set 66
4.1.2 Fuzzy if-then rules 77
4.1.3 The structure of fuzzy controller 78
4.1.4 The process of fuzzy controller design 83
4.2 Neural Network 88
4.2.1 Back propagation neural network 89
4.2.2 Radial basis function neural network 94
4.3 Adaptive Neuro-Fuzzy Inference System 95
4.3.1 Overview of ANFIS 95
4.3.2 ANFIS architecture 96
4.3.3 Hybrid learning rule 99
Chapter 5 Downlink Power Control for CDMA-based LMDS Systems 101
5.1 Downlink CDMA–based LMDS System Model 102
5.2 Distance-Based Power Control 104
5.2.1 Power reduction function 105
5.2.2 Comparison of DBPC and without PC 106
5.2.3 Transmitting power decision 107
5.3 Fuzzy-Based Power Control 110
5.3.1 Fuzzy logic control 110
5.3.2 Fuzzy-based power control scheme 115
5.4 ANFIS-based Power Control 120
5.5 Simulation Results and Discussion 127
Chapter 6 Conclusions 130
References 132

參考文獻

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指導教授

蔡木金(Mu-king Tsay)

審核日期

2008-9-19

推文