Adaptive Nonlinear Gradient Decent (angd) Algorithm for Smart Antennas

An adaptive beam former is a device, which is able to steers and modifies an array's beam pattern in order to enhance the reception of a desired signal, while simultaneously suppressing interfering signals through complex weight selection. However, the weight selection is a critical task to get the low Side Lobe Level (SLL) and Low Beam Width. It needs to have a low SLL and low beam width to reduce the antenna's energy radiation/reception ability in unintended directions. The weights can be chosen to minimize the SLL and to place nulls at certain angles. A vast number of possible window functions are available to calculate the weights for Smart Antennas. From the analysis of many of these algorithms, it is observed that there is a compromise between HPBW and SLL. But in case of smart antennas, both of these parameters must have low values to get good performance. In our earlier work it is proposed that Complex Least Mean Square (CLMS) and Augmented Complex Least Mean Square ( ACLMS) algorithms gives low beam width and side lobe level in noisy environment, however these two complex neural based algorithms have only one parameter which have a control on the convergence of the signal towards desired one. When there is only one control over the convergence of the signal, sometimes it very difficult to get optimum values for both low HPBW and SLL. This paper presents an adaptive nonlinear gradient decent algorithm in which more control parameters are used thereby optimum values can be obtained for HPBW and SLL.