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.

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.

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