Research On Adaptive Beamforming Algorithm In Smart Antenna

Smart antenna is a research hotspot in the field of modern communication, and it is used in the wireless communication systems successfully. As smart antenna imports the space division multiple Access (SDMA), it can combine with other multiple access technologies. So communication can be expanded to space domain from time domain, frequency domain and code domain. Smart antenna can increase the capacity of communication system, and utilize the limited frequency spectrum resource sufficiently.Adaptive beamforming algorithm is the key technique of smart antenna. It can control the array directional pattern adaptively to produce the narrow beam in the direction of the user signal, produce deeply null-steering in the direction of interference signals. So adaptive beamforming algorithm is the most effective method to realize the best acceptation of users signal.This thesis focuses on adaptive beamforming algorithm in smart antenna. It summarizes the previous work, and proposes some improved algorithms. Major works are as follows:Firstly, a survey on current research status of smart antenna and adaptive beamforming algorithm are given, and the adding power rules in beamforming is introduced.Secondly, the least mean square (LMS) of typical adaptive beamforming algorithm is researched, and the SVSLMS algorithm and MSVSLMS algorithm are researched and math analyzed. Facing the disadvantages of SVSLMS and MSVSLMS, an improved algorithm is proposed. The algorithm is based on hyperbolic cosine variable step LMS algorithm, defines the new step function and overcomes disadvantages of SVSLMS and MSVSLMS algorithm. Simulation results demonstrate the effectiveness of the improved algorithm.Thirdly, the kinds of cyclic adaptive beamforming (CAB) algorithms are analyzed in detail. And the performance of constrained CAB (CCAB) algorithm is poor in the case of low interfering noise ratio. In order to sovling the issue, an improved algorithm based on eigenspace-based cyclic adaptive beamforming (ECAB) algorithm. The improved algorithm utilizes subspace constraint to improve the algorithm robustness. A lot of computer simulation results demonstrate the effectiveness of the improved algorithm. Finally, the summary and the prospect on this research are given.