Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems

A semi-blind adaptive beamforming scheme is proposed for wireless systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, equal to the number of receiver antenna arrays elements, are first utilised to provide a rough initial least squares estimate of the beamformer's weight vector. A concurrent constant modulus algorithm and soft decision-directed scheme is then applied to adapt the beamformer. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study

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

Robust adaptive beamforming using a Bayesian steering vector error model

We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors

UTHM water quality classification based on sub index

River or stream at their source is unpolluted, but as water flow downstream, the river or lake is receiving point and non-point pollutant source. Ammoniacal nitrogen (NH3- N) and suspended solids (SS) strongly influences the dynamics of the dissolved oxygen in the water. Studies on monitoring this parameter were conducted for a river or lake but limited to the small man-made lake. This study is initiate to determine the changes in water quality of UTHM watershed as the water flows from upstream to downstream. The monitoring of NH3-N and TSS were monitored at two sampling schemes, 1) at the two-week interval and, 2) at a daily basis followed by the determination of the water quality sub-index particularly SIAN and SISS. The results showed that the two lakes in UTHM watershed were classified as polluted. In conclusion, the remedial action should be implemented to improve the water quality to meet the requirements at least to meet the recreational purpose