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研究生:陳伯偉
研究生(外文):Bo-way Chen
論文名稱:DS-CDMA/PRMA與DS-CDMA/FRMA第三代無線通訊系統之乏晰/類神經壅塞控制
論文名稱(外文):Fuzzy/Neural Congestion Control for Third Generation DS-CDMA Cellular Systems
指導教授:張仲儒
指導教授(外文):Chung-ju Chang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電信工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:1999
畢業學年度:87
語文別:英文
論文頁數:52
中文關鍵詞:分碼多重擷取封包預留多重擷取時框預留多重擷取通道擷取函數乏晰通道擷取函數乏晰擷取機率控制器類神經擷取機率控制器
外文關鍵詞:CDMAPRMAFRMAchannel access functionfuzzy channel access functionfuzzy access probability controllerneural-net access probability controller
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在第三代無線通訊系統中,能否提供較大的頻寬、較高的傳輸速率及傳送多媒體資料已成為主要的設計考量。其中分碼多工擷取 (CDMA) 的系統由於具有高度頻寬使用效率、抵抗多路線干擾以及低功率傳輸的能力等,已成為未來無線通訊系統的一個選擇。而對於直接序列-分碼多工擷取 (DS-CDMA) 來說,系統的容量是受到多重擷取干擾限制的,如何去控制干擾量就是一個重要的課題。
在本篇論文中,我們首先在直接序列-分碼多工擷取/封包保留多工擷取 (DS-CDMA/PRMA) 環境下使用乏晰邏輯 (fuzzy logic) 設計壅塞控制器。此控制器基於傳統通道擷取函數 (channel access function) 的知識,給予使用者更適當的擷取機率 (access probability),使得傳送失敗率 (corruption ratio) 降低,因而有較好的表現。再來我們在直接序列-分碼多工擷取/時框保留多工擷取 (DS-CDMA/FRMA) 環境中設計一個回授壅塞控制器。這個控制器包含了平行迴路類神經網路 (pipeline recurrent neural network) 干擾預測器、乏晰效能指標器 (fuzzy performance indicator) 以及擷取機率控制器 (APC)。有了這個干擾預測器的幫助,對於系統干擾量的控制將會更有效。針對其中的擷取機率控制器我們提出了兩種機制來設計,一為乏晰擷取機率控制器 (FAPC),另一為類神經網路擷取機率控制器 (NAPC)。藉由預測的干擾值及系統效能指標器的回授,乏晰擷取機率控制器與類神經網路擷取機率控制器可以恰當地調整擷取機率來控制競爭使用者的人數以使干擾量低於某個標準。由模擬的結果中,我們可以看到我們所設計的智慧型壅塞控制器在傳送失敗率、語音封包漏失率及系統使用效率上,都有較好的表現。

In this thesis, we study congestion control in DS-CDMA cellular systems. We propose a fuzzy technique for congestion control in DS-CDMA with PRMA (packet reservation multiple access) as its MAC (medium access control) protocol. We design a fuzzy congestion controller, named fuzzy channel access function (FCAF), based on knowledge from the channel access function (CAF) used in conventional DS-CDMA/PRMA cellular systems. We also design a fuzzy/neural congestion control for DS-CDMA/FRMA (frame reservation multiple access), where FRMA evolves from PRMA. FRMA separates the contention traffic from the reservation traffic so that the reservation traffic will not be disturbed by the contention traffic,
unlike PRMA. The fuzzy/neural congestion controller is constituted by a pipeline recurrent neural network (PRNN) interference predictor, a fuzzy performance indicator, and an access probability controller (APC). This APC can be either fuzzy access probability controller (FAPC) or neural-net access probability controller (NAPC). By taking more system
performance parameters into consideration, the access probabilities given by FAPC and NAPC can be more accurate. Simulation results show that the DS-CDMA/PRMA system with FCAF performs better than that with conventional CAF in overall performance. Furthermore, the DS-CDMA/FRMA system with fuzzy/neural congestion controller overrides the
DS-CDMA/PRMA system with CAF in the voice packet dropping ratio, the corruption ratio, the utilization, and data packet delay. This approach also outperforms the DS-CDMA/PRMA system with FCAF in the voice packet dropping ratio under medium traffic load, the corruption ratio and the utilization for all traffic loads. Moreover, NAPC outperforms FAPC. If
the requirement of the voice packet dropping ratio is set to be $10^{-2}$, the DS-CDMA/FRMA system with NAPC has an improvement of $10.76\%$ in capacity, the DS-CDMA/FRMA system with FAPC has $7.59\%$ improvement, and the DS-CDMA/PRMA system with FCAF has $5.06\%$ improvement, with comparison to the DS-CDMA/PRMA system with CAF.

Chapter 1: Introduction
Chapter 2: Fuzzy Congestion Control for a DS-CDMA/PRMA Cellular System
2.1: Introduction
2.2: System Model
2.3: Fuzzy Congestion Controller
2.3.1: Fuzzy Logic System
2.3.2: Fuzzy Channel Access Function (FCAF)
2.4: Simulation Results and Discussions
2.5: Concluding Remarks
Chapter 3: Fuzzy/Neural Congestion Control for a DS-CDMA/FRMA Cellular System
3.1: Introduction
3.2: System Model
3.3: Fuzzy/Neural Congestion Controller
3.3.1: PRNN Interference Predictor
3.3.2: Fuzzy Performance Indicator
3.3.3: Fuzzy/Neural Access Probability Controller
3.4: Simulation Results and Discussions
3.5: Concluding Remarks
Chapter 4: Conclusion

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