臺灣博碩士論文加值系統

本篇論文探討DS-CDMA蜂巢式行動電話系統中的通道預測以及功率控制。為了提高頻率的使用效率，第三代行動通訊系統的無線資源必須根據所測的鏈路增益（link gain）來分配，然通道測量有不可避免的迴圈延遲，因此加入預測器可達到延遲補償以增加測量的準確性。另外，功率控制可以解決距離遠近效應以及短期訊號衰退效應以確保需要的通訊品質，然訊號估測的迴圈延遲依然存在，因此預測器的加入可以增加功率控制的效能。
首先我們提出PRNN/ERLS來做為通道預測的預測器，ERLS是PRNN架構下係數調整的機制，該機制的優點是係數的調整除了根據現在的預測誤差外，也考慮到過去的誤差，因而在這樣的機制下，係數可避免無謂的調整，而有效地減少預測誤差。再來我們提出以PRNN/ERLS預測鏈路增益（link gain）的功率控制，相對於預測接收的SIR（received SIR）的功率控制，鏈路增益不會受到本身功率控制的影響，故可達到真正的延遲補償以減少預測誤差，此外，也因PRNN/ERLS可用到更多相關性高且幾乎不變動的訊號，因此預測誤差可再次減少，而系統容量也會隨著功率控制的改善而提升。

In this thesis, the channel prediction and power control for DS-CDMA cellular mobile system are designed. In order to increase frequency utilization, the third generation wireless communication system must allocate its radio resource according to estimated link gain. However, the channel estimation cannot avoid loop delay, and thus joining predictor can improve its estimation and achieve delay compensation. On the other hand, power control can counteract the near-far effect and short-term fading to maintain desired communication quality, but the loop delay of signal estimation still exists. Hence, joining predictor can improve power control.
First, we propose PRNN with ERLS as a predictor for channel prediction. ERLS is a weight update algorithm for PRNN scheme. The virtue of this algorithm is the weight update is based on not only present prediction error but also previous ones. Hence, this algorithm can avoid unnecessary weight adjustment, and thus its prediction error can be effectively reduced. Second, we propose power control with PRNN/ERLS for link gain prediction. Compared with received SIR prediction-based scheme, the link gain cannot be interfered from itself power control scheme, and thus it can achieve real delay compensation to reduce prediction error. Moreover, PRNN with ERLS can use more signals that are high correlative and almost stationary. Hence, the prediction error can be reduced again, and thus the system capacity can increase as the improvement of power control.

Chinese Abstract i
English Abstract ii
Acknowledgment iii
Contents iv
List of Figures vi
List of Tables viii
1 Introduction 1
2 PRNN/ERLS-Based Channel Prediction 5
2.1 Introduction 5
2.2 Pipelined Recurrent Neural Network with Extended Recursive Least Squares 7
2.2.1 PRNN 8
2.2.2 Learning Algorithm of PRNN: GD and ERLS 11
2.2.3 The Virtue of ERLS 14
2.2.4 RLS 15
2.3 System Model 17
2.3.1 Channel Model 17
2.3.2 Traffic Model 19
2.3.3 Simulation Model 20
2.4 Simulation Results and Discussions 22
2.5 Concluding Remarks 27
3 Power Control with Link Gain Prediction using PRNN/ERLS 28
3.1 Introduction 28
3.2 Conventional Received SIR Prediction-Based Power Control 30
3.3 Link Gain Prediction-Based Power Control using PRNN/ERLS 34
3.3.1 Link Gain Prediction-Based Power Control 35
3.3.2 PRNN with ERLS 37
3.4 Simulation Results and Discussions 38
3.5 Concluding Remarks 46
4 Conclusion 48

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