scholarly journals Improved Linearly Constrained Minimum Variance Algorithm for 5G Communications System

2021 ◽  
Author(s):  
Kaviya K R ◽  
Deepa S
Keyword(s):  
Comparative Analysis ◽  
Smart Antenna ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Key Technology ◽  
The Core ◽  
Linearly Constrained ◽  
Communications System ◽  
Beam Patterns ◽  
Minimum Interference

Beamforming is a process formulated to produce the radiated beam patterns of the antennas by completely building up the processed signals in the direction of the desired terminals and cancelling beams of interfering signals. Adaptive beamforming is a key technology of smart antenna. The core is to obtain optimum weights of the antenna array by some adaptive beamforming algorithms and finally adjust the main lobe to focus on the arriving direction of the desired signal as well as suppressing the interfering signal. There are several beamforming algorithms that includes Linearly Constrained Minimum Variance (LCMV) algorithm in which Self Nulling Issue is further reduced by adding multiplier to the MCMV algorithm and it is referred as Improved LCMV (IMPLCMV). A Comparative analysis is done for different multipliers and it is found that w=0.15 gives best result with minimum interference of flat response and also self-nulling issues can be reduced.

scholarly journals Evaluation of Minimum Variance Distortionless Response Beamforming Algorithm Based Circular Antenna Arrays

2016 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Suhail Najm Shahab ◽  
Ayib Rosdi Zainun ◽  
Balasim S. S. ◽  
Nurul Hazlina Noordin ◽  
Izzeldin Ibrahim Mohamed
Keyword(s):  
Antenna Arrays ◽  
Smart Antenna ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Antenna System ◽  
Noise Power ◽  
Noise Mitigation ◽  
Lte Networks ◽  
Continuous Growth ◽  
Minimum Variance Distortionless Response

Wireless data traffic is in a continuous growth, and there are increasing demands for wireless systems that provide deep interference suppression and noise mitigation. In this paper, adaptive beamforming (ABF) technique for Smart Antenna System (SAS) based on Minimum Variance Distortionless Response (MVDR) algorithm connected toCircular Antenna Array (CAA) is discussed and analyzed. The MVDR performance is evaluated by varying various parameters; namely the number of antenna elements, space separation between the elements, the number of interference sources, noise power label, and a number of snapshots. LTE networks allocate a spectrum band of 2.6 GHz is used for evaluating the MVDR performance. The MVDR performance is evaluated with two important metrics; beampattern and SINR. Simulation results demonstrate that as the antenna elements increase, the performance of the MVDR improves dramatically. This means the performance of MVDR greatly relies upon the number of the elements. Half of the wavelength is considered the best interelement spacing, the performance degraded as noise power increased, and more accurately resolution occurred when the number of snapshots increased. The proposed method was found to be performed better than some existing techniques. According to the result, the beampattern relies on the number of element and the separation between array elements. Also, the SINR strongly depends on noise power label and the number of snapshots.

scholarly journals Adaptive Beamforming Based on Compressed Sensing with Smoothedl0Norm

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yubing Han ◽  
Jian Wang
Keyword(s):  
Compressed Sensing ◽  
Large Scale ◽  
Monte Carlo Analysis ◽  
Minimum Variance ◽  
Adaptive Beamforming ◽  
Reconstruction Method ◽  
Planar Arrays ◽  
Linearly Constrained ◽  
Spatial Sparsity ◽  
Beamforming Algorithm

An adaptive beamforming based on compressed sensing with smoothedl0norm for large-scale sparse receiving array is proposed in this paper. Because of the spatial sparsity of the arriving signal, compressed sensing is applied to sample received signals with a sparse array and reduced channels. The signal of full array is reconstructed by using a compressed sensing reconstruction method based on smoothedl0norm. Then an iterative linearly constrained minimum variance beamforming algorithm is adopted to form antenna beam, whose main lobe is steered to the desired direction and nulls to the directions of interferences. Simulation results and Monte Carlo analysis for linear and planar arrays show that the beam performances of our proposed adaptive beamforming are similar to those of full array antenna.

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