A Wideband Direct Data Domain Genetic Algorithm Beamforming

In this paper, a wideband direct data-domain genetic algorithm beamforming is presented. Received wideband signals are decomposed to a set of narrow sub-bands using fast Fourier transform. Each sub-band is transformed to a reference frequency using the steering vector transformation. So, narrowband approaches could be used for any of these sub-bands. Hence, the direct data-domain genetic algorithm beamforming can be used to form a single ‘hybrid’ beam pattern with sufficiently deep nulls in order to separate and reconstruct frequency components of the signal of interest efficiently. The proposed approach avoids most of drawbacks of already-existing statistical and gradient-based approaches since formation of a covariance matrix is not needed, and a genetic algorithm is used to solve the beamforming problem

Adaptive beamforming for large arrays in satellite communications systems with dispersed coverage

Conventional multibeam satellite communications systems ensure coverage of wide areas through multiple fixed beams where all users inside a beam share the same bandwidth. We consider a new and more flexible system where each user is assigned his own beam, and the users can be very geographically dispersed. This is achieved through the use of a large direct radiating array (DRA) coupled with adaptive beamforming so as to reject interferences and to provide a maximal gain to the user of interest. New fast-converging adaptive beamforming algorithms are presented, which allow to obtain good signal to interference and noise ratio (SINR) with a number of snapshots much lower than the number of antennas in the array. These beamformers are evaluated on reference scenarios

Linear Beamforming for the Spatially Correlated MISO broadcast channel

A spatially correlated broadcast setting with M antennas at the base station and M users (each with a single antenna) is considered. We assume that the users have perfect channel information about their links and the base station has only statistical information about each user's link. The base station employs a linear beamforming strategy with one spatial eigen-mode allocated to each user. The goal of this work is to understand the structure of the beamforming vectors that maximize the ergodic sum-rate achieved by treating interference as noise. In the M = 2 case, we first fix the beamforming vectors and compute the ergodic sum-rate in closed-form as a function of the channel statistics. We then show that the optimal beamforming vectors are the dominant generalized eigenvectors of the covariance matrices of the two links. It is difficult to obtain intuition on the structure of the optimal beamforming vectors for M > 2 due to the complicated nature of the sum-rate expression. Nevertheless, in the case of asymptotic M, we show that the optimal beamforming vectors have to satisfy a set of fixed-point equations.Comment: Published in IEEE ISIT 2010, 5 page