Partial Parallel Interference Cancellation for DS-CDMA Multiuser Detection: Performance Analysis and New Algorithms

• Main Authors: Yu-Tao Hsieh, 謝雨滔
• Other Authors: Wen-Rong Wu
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/f66p6z

博士 === 國立交通大學 === 電信工程系所 === 92 === 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.