Two‐dimensional adaptive beamforming in DS‐CDMA communication with local scattering channels

Abstract

Since the antenna array at base station is typically mounted on the roof of a high building, the mobile users are not located at the same level as the antenna array. As the mobile users near the base station, a two-dimensional (2-D) array is usually required for effective adaptive beamforming, especially for sources with the same azimuth angles but different elevation angles with respect to the antenna array. In direct sequence code-division multiple access (DS-CDMA) communication, different local scatterers in the vicinity of the mobile sources cause angular spread of radiating signal as seen from a base station antenna array. The multipath propagation from coherent local scatterers will result in a steering vector which is not composed of arriving angles of original radiating sources alone. Applying first-order Taylor expansion to the array steering vector with local scattering, we provide a theoretical solution for a 2-D generalized plane-type array manifold (GPAM). Computer simulations demonstrate the effectiveness of the proposed 2-D adaptive beamforming in DS-CDMA communication with local scattering channels.

Subspace‐based techniques for 2‐d DOA estimation with uniform circular array under local scattering

“…There have been many 2-D direction estimation algorithms, such as the 2-D multiple signal classification (MUSIC) (Schmidt, 1986) algorithm and the 2-D Estimation of signal parameters via rotational invariance techniques (ESPRIT) (Yin et al, 1989) algorithm, but they are not suitable for DOA estimation in local scattering channels. To cope with this problem, there have been some studies proposing uniform linear array (ULA) geometry and uniform planar array (UPA) geometry (Chang and Chiang, 2003) in local scattering channels. They use a first-order Taylor expansion to linearize the nonlinear conventional array manifold (CAM) produced by scatterers and develop the generalized array manifold (GAM) model which can obtain better nominal DOA estimation for mobiles.…”

“…The MUSIC algorithm estimates DOA by searching the array manifold for direction vectors that are orthogonal to the noise subspace. However, the MUSIC algorithm has a major impact on high resolution DOA estimation problems, meaning that the computational complexity is high (Chang and Chiang, 2003). The original ESPRIT (Roy and Kailath, 1989) algorithm with low computational complexity, which can be applied to UCA for 2-D angle estimation was studied in (Swindlehurst, 1992).…”