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研究生:蔡私付
研究生(外文):Szu-Fu Tsai
論文名稱:AdaptiveNeuron-FuzzyParallelInterferenceCancellationforDS-CDMARadioSystem
論文名稱(外文):應用類神經模糊控制系統於分碼多工通信系統之平行干擾消除技術
指導教授:連振凱連振凱引用關係許明華許明華引用關係
指導教授(外文):Jenn-Kaie LainMing-Hwa Sheu
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電子與資訊工程研究所碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:92
語文別:英文
論文頁數:67
中文關鍵詞:類神經模糊控制系統分碼多工通信系統之平行干擾消除技術
外文關鍵詞:Parallel Interference CancellationAdaptive Neuron-FuzzyDS-CDMA Radio System
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We investigate the technologies of multiuser interference cancellation technique for DS-CDMA radio systems. One of main factors that affect the performance of DS-CDMA systems is the multiple access interferences (MAIs). Multiuser interference calculation technique can eliminate the MAIs and boost high spectral efficiency radio communications. Several suboptimal multiuser interference cancellation schemes have been proposed. Among the suboptimal multiuser interference cancellation schemes, the multistage interference cancellation scheme cancels the MAIs by parallel way and performs very well. In this dissertation, the performance for that suboptimal multiuser interference cancellation with multistage interference cancellation will be investigated. Based on the study results, we try to design a new multiuser interference cancellation scheme using neuro-fuzzy theory that is suitable for realization by using DSP processors or VLSI. Meanwhile, the conventional auto-retransmission request (ARQ) adopted in the wire-line communications is not suitable to satellite communications owing to the inherent round-trip delay. Instead of ARQ, a powerful error control code is often required in wireless communications. We further combine the above-mentioned neruo-fuzzy-based multistage multiuser interference cancellation technique and the Goal codes to possibly enhance the spectral efficiency wireless communications.
在DS-CDMA無線通信系統中,多重接取干擾(Multiple Access Interference, MAI)是影響其效能的重要因素,而多用戶干擾消除技術則是對抗此種干擾的重要技術之一。目前文獻中提出的次佳多用戶干擾消除技術(Suboptimal Multiuser Interference Cancellation)各有優缺點,大多數的多用戶干擾消除技術要達到良好效能時,其計算複雜度通常很高,以致不易實現。在這些次佳多用戶干擾消除技術中,多級干擾消除(Multistage Interference Cancellation, MIC)架構以非線性之方式平行地(Parallel)將MAI消除,其檢測效能相當不錯,且在平行處理器上實現時所需之計算複雜度也不高,且一次同時進行所有使用者的訊號檢測,相當適合於高速傳送系統使用。然而,由於前級之干擾消除時,會因干擾之可靠度不高而造成誤差傳遞(Error Propagation),因此,我們將應用類神經模糊 (Neuro-Fuzzy) 理論作決策,經由通道估測(Channel Estimation)之資訊來推論出最佳之干擾可靠度參數,使得在作平行干擾消除時可得到最好之效能,並期能設計出效能佳、計算量不高且利於用DSP處理器或VLSI實現之多用戶干擾消除架構。同時,無線通信通訊由於訊號傳遞延遲(Round-trip Delay)的特性,因此在錯誤控制部分不適合採用一般地面通訊中經常採用的ARQ技術,而需要採用錯誤更正能力強大的錯誤更正碼。我們將結合上述以Neuro-Fuzzy為基礎的多級多用戶干擾消除技術與錯誤更正能力強大的Goal Codes,以更進一步提高無線通信通訊的頻譜使用效率。
Abstract i
摘要 iii
Abstract i
摘要 iii
Contents v
List of Figures viii
List of Tables x
Chapter1 1
Introduction 1
1.1 MOTIVATIONS 3
1.2 ORGANIZATION OF THIS THESIS 4
Chpter2 5
DS-CDMA System Modeling 5
2.1 GOLD CODE SEQUENCE GENERATION 7
2.2 TRANSMITTER MODEL 8
2.3 CHANNEL MODEL 10
2.4 RECEIVER MODEL 12
2.5 CONCLUSION 15
Chapter3 16
Partial Parallel Interference Cancellation Multiuser Detection 16
3.1 CONVENTIONAL PARALLEL INTERFERENCE CANCELLATION 18
3.1.1 CPIC in Flat Fading Channels 18
3.1.2 CPIC in Frequency Selective Fading Channels 20
3.2 PARTIAL PARALLEL INTERFERENCE CANCELLATION 22
3.2.1 CW-PPIC in Flat Fading Channels 22
3.2.2 CW-PPIC in Frequency Selective Fading Channels 23
3.3 FUZZY-BASED PARALLEL INTERFERENCE CANCELLATION 24
3.3.1 Fuzzy-Based PPIC in a Flat Fading Channel 24
3.3.2 Fuzzy-Based PPIC in Frequency Selective Fading Channels 28
3.4 CONCLUSION 30
Chapter 4 31
Adaptive Neuro-Fuzzy Partial Parallel Interference Cancellation for DS-CDMA Radio system 31
4.1 NEURAL NETWORKS 33
4.1.1 Backpropagation Neural Networks (BPNN) 33
4.1.2 Radial Basis Function Neural networks 35
4.2 ADAPTIVE NEURAL FUZZY INFERENCE SYSTEMS 38
4.2.1 The First Adaptive Neural Fuzzy Inference Systems 39
4.2.1.2 Supervised Learning Algorithm in a Neural Fuzzy Network 42
4.2.2 The Second Adaptive Neural Fuzzy Inference Systems 44
4.2.2.2 Supervised Learning Algorithm for second Neural Fuzzy Network 46
4.2.3 The Third Adaptive Neural Fuzzy Inference Systems 49
4.2.4.2 Supervised Learning Algorithm for third Neural Fuzzy Network 50
4.3 NEURO-FUZZY PARALLEL INTERFERENCE CANCELLATION 53
4.4 CONCLUSION 58
Chapter 5 59
Comparisons of Simulation Results in Severely PPIC Schemes 59
5.1 SIMULATIONS FOR PERFECT POWER CONTROL SITUATION 60
5.1.1 Analysis for the First NFB-PPIC 61
5.1.2 Analysis for the Second NFB-PPIC 62
5.1.3 Analysis for the Third NFB-PPIC 63
5.2 CONCLUSION 64
Reference 65
























List of Figures
Figure 2.1 DS-CDMA wireless communication system model 5
Figure 2.2 Functional block of gold code generator 7
Fig. 2.3 The transmitter model and channel model for asynchronous CDMA systems 10
Figure 2.4 Architecture of conventional detector for K user synchronous CDMA systems 12
Figure 2.5 The conventional Rake receiver combine with MRC technique in a frequency selective fading channel of two paths for synchronous CDMA system 14
Figure 3.1 Multiuser detector architecture of conventional multistage PIC secheme for synchronous CDMA systems 18
Figure 3.3(a) Membership function ffor SNR of interfere (dB) 25
Figure 3.3(b) Membership function for effective number of users ( ) 26
Figure 3.3(c) Membership function for PCW 26
Figure 3.6 Block diagram of two-stage PPIC receiver based on FIS with MRC for synchronous CDMA system over a two-path frequency selective fading channel. 29
Figure 4.1 categorization of neural network according to learning type 33
Figure 4.2 BPNN Structure with i inputs of Node j at layer n 34
Figure 4.3 Activation functions for BNPP 35
Figure 4.4 The RBF NN architecture 36
Figure 4.5 First Structure of a neural fuzzy inference system with five layers input layer: 40
Figure 4.9 adaptive neural fuzzy-based PPIC multiuser detection scheme 54
Figure 5.1 BER vs the user capacity for various multistage PIC schemes with perfect power control and SNR=6dB. 61
Figure5.2 BER vs the SNR for various multistage PIC schemes with perfect power control and number of user K=20. 61
Figure 5.3 BER vs the user capacity for various multistage PIC schemes with perfect power control and SNR=6dB. 62
Figure5.4 BER vs the SNR for various multistage PIC schemes with perfect power control and number of user K=20. 62
Figure 5.5 BER vs the user capacity for various multistage PIC schemes with perfect power control and SNR=6dB. 63
Figure5.6 BER vs the SNR for various multistage PIC schemes with perfect power control and number of user K=20. 63










List of Tables
Table 2.1 Short list of preferred pairsfor Goal sequence generator 8
Table 3.1 Rule base of FIS in stage2 27
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