適用於直接序列碼分多重擷取系統多用戶偵測之部分平行式干擾消除：效能分析與新演算法

Abstract

平行式干擾消除法乃是針對直接序列碼分多重擷取系統一簡單而有效之多用戶偵測器。然而其效能表現可能因前幾階不可靠之干擾消除而降低，因此就有部分平行式干擾消除法的發展，此法乃利用部分消除因子來控制欲消除之干擾量，而提高系統效能。雖然部分消除因子佔有關鍵地位，然其完整的最佳解尚未有深入探討。本論文重點即在於針對不同形式之部分平行式干擾消除法，求得其最佳消除因子值，並進行效能分析。在論文的第一部份，吾人考慮一個二階式軟決策部分平行式干擾消除接收機，利用最低位元錯誤率的條件，吾人導證出完整的部分消除因子解，其中包括了週期碼、非週期碼系統，並適用於白高斯通道，與多重路徑通道。實驗結果顯示，經由理論求得之最佳部分消除因子值與實際值相當接近。此利用最佳部分消除因子值之二階式部分平行式干擾消除法不僅優於二階全平行式干擾消除法，亦優於三階全平行式干擾消除法。在論文的第二部分，吾人分析二階適應性盲蔽型硬決策部分平行式干擾消除法。在此架構中，經調適過而被用作最佳消除因子之權重值，乃是由最小均方理論訓練而得。吾人推導出最佳權重值、權重值之平均誤差、及其均方差值。根據這些理論結果，吾人得到每個使用者之輸出信號均方差及位元錯誤率。步階值在最小均方理論的收斂行為中，扮演著關鍵角色，對部分平行式干擾消除法的系統效能也影響甚鉅。藉著所推導之輸出信號均方差，吾人可以求得最佳步階值。在論文的最後一部份，吾人針對適應性盲蔽型硬決策部分平行式干擾消除法，提出一改善方法，其主要概念在於減低最小均方理論中所訓練之權重值的數目，並且進行權重值之後續濾波處理，使得最終多餘的均方差能因此減低。吾人也推導改良理論之輸出均方差與位元錯誤率。實驗結果證實所提出之改良理論表現優於傳統部分平行式干擾消除法，而理論分析結果也相當準確。

Parallel interference cancellation (PIC) is considered a simple yet effective multiuser detector for direct-sequence code-division multiple-access (DS-CDMA) systems. However, its performance may deteriorate due to unreliable interference cancellation in the early stages. Thus, a partial PIC detector in which partial cancellation factors (PCFs) are introduced to control the interference cancellation level has been developed as a remedy. Although PCFs are crucial, complete solutions for their optimal values are not available. In this dissertation we focus on the determination of optimal PCFs and performance analysis for various partial PICs. In the first part of the work, we consider a two-stage soft-decision partial PIC receiver. Using the minimum bit error rate (BER) criterion, we derive a complete set of optimal PCFs in the second stage. This includes optimal PCFs for periodic and aperiodic spreading codes in additive white Gaussian channels and multipath channels. Simulation results show that our theoretical optimal PCFs agree closely with empirical ones. Our two-stage partial PIC using derived optimal PCFs outperforms not only a two-stage, but also a three-stage full PIC. In the second part of the work, we analyze the performance of a two-stage adaptive blind hard-decision partial PIC. In this scheme, the adapted weights serving as optimal PCFs are trained using the least mean square (LMS) algorithm. We derive the analytical results for optimal weights, weight error means, and weight error variances. Based on these results, we also derive the output mean square error (MSE) and BER for each user. The step size known to be a critical parameter in the LMS algorithm controls the LMS convergence behavior and partial PIC performance. Using the output MSE criterion, we can then optimize the step size. Simulation results indicates that our analytical results can well match with empirical ones. In the final part of the work, we propose an improved adaptive blind hard-decision partial PIC and analyze its performance. The main idea is to reduce the number of active weights in the LMS algorithm and to perform weight post filtering such that the resultant excess MSE can be reduced. We also derive the output MSE and BER for the proposed algorithm. Simulation results verify that the proposed algorithm outperforms the conventional partial PIC approach and analytical results are accurate.