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MC-CDMA systems interference cancellation technology

Author: LongYinFang
Tutor: ZhaoZhiJin；YangHaiBo
School: Hangzhou University of Electronic Science and Technology
Course: Signal and Information Processing
Keywords: Multi-carrier code division multiple access MAI ICI Multiuser Detection Offset Estimation PSO
CLC: TN914.53
Type: Master's thesis
Year: 2009
Downloads: 70
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Abstract


MC-CDMA systems with CDMA technology and OFDM technology advantages, with a high spectral efficiency, the data transmission rate, robustness against frequency selective fading characteristics, the next generation mobile communication of multi-carrier transmission scheme. Although MC-CDMA systems have many advantages, but it also inherited deficiencies of these two technologies. MAI affecting the major interference in CDMA system capacity, OFDM technology is one of the major deficiencies carrier frequency offset is very sensitive to the user, even a very small frequency offset will cause the sub-carrier interference, thereby deteriorating the system performance. Due to MC-CDMA systems using CDMA and OFDM technologies, there are multiple access interference and subcarrier interference, so MC-CDMA systems interference cancellation technology research has become a hot topic in academia. This paper studies the MC-CDMA systems in the two main interference, ie multiple access interference and subcarrier interference cancellation. First, in the case of carrier synchronization system derived MC-CDMA systems signal model, drawing on multi-user detection in CDMA systems thinking, the MC-CDMA systems for multi-user detection problem as a combinatorial optimization problem, and apply discrete particle swarm algorithm for the optimal solution. Basic particle swarm algorithm to solve the problem when parameter setting is simple, easy to implement, but it is easy to fall into local convergence and convergence speed is not fast enough, so the neural network algorithm into discrete particle swarm algorithm to increase the diversity of particle improved particle swarm algorithm optimization ability and convergence speed is proposed based on neural networks PSO MC-CDMA systems in multi-user detection. Simulation results show that the neural network-based particle swarm algorithm MC-CDMA systems is superior to multi-user detection algorithm based on particle swarm performance and multi-user detection based on neural network multi-user detection performance. Secondly, the analysis of the carrier frequency offset when there is a multi-user MC-CDMA systems signal receiver model, due to the existence of multi-user signal OFDM system model and signal model is different, so OFDM system Offset Estimation Algorithm for MC-CDMA systems can not be applied directly, need to make some improvements. Based on the maximum likelihood criterion, ESR frequency estimation maximum likelihood estimation expressions, and see it as a continuous optimization problem, using continuous particle swarm optimization algorithm for solving the frequency offset estimate. Proposed based on particle swarm optimization and pilot symbols MC-CDMA systems Frequency Offset Estimation Algorithm. Simulation results show that for MC-CDMA systems frequency offset estimation, this paper based on particle swarm optimization and pilot symbols outperforms frequency offset estimation based on virtual sub-carrier frequency offset estimation. Finally, since the MC-CDMA systems in multiple access interference and subcarrier interference effects are linked to each other, we propose a carrier frequency offset and user data joint estimation method. In the maximum likelihood criterion is derived carrier frequency offset estimation expression and user data, and then use with compression factor particle swarm optimization algorithm for the optimal solution. Simulation results show that the proposed joint estimation methods of multi-user detection error rate is much lower than the frequency offset compensation is not considered multi-user detection methods, frequency offset estimate the mean square error approach to the use of particle swarm optimization and frequency of pilot symbols bias estimation method, the present method does not require any training sequence and a pilot symbol, high spectral efficiency.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication systems ( transmission system) > Multiple access communication system > Code Division Multiple Access (CDMA) communications
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