Research On Adaptive Beamforming Algorithm Of Smart Antenna

With the capacity demands and further improvement of quality requirements on the wireless communication, as one of the key technologies solving the difficult problem, the smart antenna has played an increasingly significant role.The adaptive beamforming algorithm is the core and key technology of the smart antenna, this thesis focuses on the adaptive beamforming technology. First of all, the thesis introduces research background and trends of the smart antenna, sketches the basic operation principle of the smart antenna and signal model, and has analyzed the advantages of the smart antenna. Then, being based on the best adaptive beamforming criterion, the thesis introduces emphatically, analyzes and contrasts the several common non-blind adaptive beamforming algorithms; For traditional LMS algorithm, affected by the step size in convergence speed, tracking abilities of time-varying system and steady state imbalance between contradictions, the thesis has given a kind of new variable step size LMS algorithm; The MATLAB simulation results prove that the new variable step size LMS algorithm in convergence speed, steady state and barrage jamming has been further improvement than the traditional LMS algorithm.Finally, the thesis introduces the blind adaptive beamforming algorithms in detail, the SGD-CMA algorithm by step size in convergence performance and steady state of the contradiction between the problem, for the reason a new variable step size SGD-CMA algorithm is given. The MATLAB simulation results prove that the new variable step size algorithm has solved the contradiction between convergence speed and steady state for the SGD-CMA algorithm, and the convergence performance of the algorithm is improved; Based on the shortcoming of capturing the interference signals of the common blind adaptive beamforming algorithm, the new variable step size SGD-CMA algorithm being based on the minimal Rayleigh entropy principle is given further, the MATLAB simulation results demonstrate that the new algorithm is effective to solve the constant modulus algorithms existing problems of capturing the interference signals, and the problems of the algorithm converging to the local minimum points and convergence speed slowly. The algorithm in strong interference can still work well.