Smart Adaptive Array Antennas For Wireless Communications

Smart adaptive antenna technology is considered to be the last technology frontier that has the potential of leading to large increases in systems performance. Time domain techniques have been extensively exploited. Space domain techniques, on the other hand, have not been exploited to the same extent. When applied to wireless, the benefits of smart adaptive array a antennas are as follows: (i) increased covered, which is important in the early stages of life cycle, (ii) increased capacity, which is important in the later stages of life cycle, (iii) improved link quality, (iv) reduced costs and increased return on investment, (v) lower handset power consumption, and (vi) assistance in user location by means of direction finding. This paper discusses an experimental neural network based smart antenna capable of performing direction finding and the necessary beamforming. The Radial Basis Function Neural Network (RBFNN) algorithm is used for both tasks and for multiple signals. The algorithm operates in two stages. The field of view of the antenna array is divided into spatial sectors, then each network is trained in the first stage to detect signals emanating from sources in that sector. According to the outputs of the first stage, one or more networks of the second stage can be activated so as to estimate the exact location of the sources. No a priori knowledge is required about the number of sources, and the networks can be designed to arbitrary angular resolution. Some experimental results are shown and compared with other algorithms, such as, the Fourier Transform and the MUSIC algorithm. The comparisons show the superior performance of the RBFNN and its ability to overcome many limitations of the conventional and other superresolution techniques, specifically by reducing the computational complexity and the ability to deal with a large number of sources.