What is it about?
With the proposed algorithm, an array beamforming vector is partitioned into a number of sub-vectors of small sizes, allowing a reduced-dimension processing.
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Why is it important?
The CSVO does not avoid computing the matrix inversion but it can flexibly select the size of a sub-vector for the computation. In comparison with the DMI, the CSVO reduces the size of the matrix at the designer’s choice, processing with a lower computational load. In comparison with the SLMS or the SARLS, the CSVO needs to compute the matrix inversions with selected sizes, slightly increasing the computational load. But the CSVO gains a faster convergence rate and an improved stability. With a different optimization mechanism, the CSVO also outperforms the SSRLS in convergence, stability and computational complexity. It is interesting to note that when using only one sub-vector with one optimization cycle, the CSVO virtually becomes the DMI. Then the DMI can be viewed as a special case of the CSVO.
Read the Original
This page is a summary of: MMSE Beamforming with Cyclical Sub-Vector Optimization, IET Signal Processing, March 2020, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-spr.2019.0164.
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