Smart Antenna Adaptive Beamforming Algorithm Analysis And Research

In recent years, the rapid development of the mobile communication promotes the progress of the information age greatly and brings great impact to people’s life. But, Multi-channel fading, multiple access interference and co-channel interference effects severely limit the communication quality and channel capacity for further improvement. Existing technology seems to have been not solve the above problems. However, the proposed smart antenna technology has brought new ideas and methods to solve these problems. The use of smart antenna technology is the spatial characteristic of signal, namely the use of signal arrival angle to achieve the purpose of overcoming the above factors, to ensure the quality of original communication and improve the coverage significantly, while also increasing the system capacityAdaptive beamforming algorithm is one of the core technology of smart antenna. However, the many factors of the algorithm, such as convergence, steady-state, the computational complexity and other factors, will also have a direct impact on overall system performance. Therefore, this article will study from the convergence and steady-state of algorithm.This paper introduces the basic structure of smart antenna and how it works firstly, and introduces the array signal model in the work environment, Then introduces several convergence criteria of the smart antenna. And several classic algorithm based on the guidelines,such as minimum mean-square(LMS)algorithm, recursive least square(RLS)algorithm and sample matrix inverse(SMI) algorithm. Secondly, two variable step size LMS algorithms based on non-linear function are analysed, the variation of the step size is analysed through the mathematical function. A new inverse hyperbolic sine function variable step size LMS algorithm is presented in view of these two kinds of algorithm, and the simulation result demonstrate the superiority of the algorithm. Finally, according to stochastic gradient constant modulus algorithm’s contradictions between the convergence and the steady state error, a new step iterative above is applied to the stochastic gradient constant modulus algorithm in this paper, through the simulation experiments, the defect of SGD-CMA can be solved better. In beamforming, the two algorithm of this article can form a main lobe in the direction of desired signals, generate null in the direction of undesired signals, have a better inhibition to the interference signals.