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The recently proposed Laplacian twin support vector machine (Lap-TSVM) is an excellent nonparallel-based kernel tool for semi-supervised classification problems, where its optimal decision hyperplane is determined by solving two quadratic programming problems (QPPs) with matrix inversion operations.
Consequently, the recent research such as [6-8] fonused on reducing the complexity of V-BLAST receiver architectures for frequency selective channels by developing efficient matrix inversion operations.
As percentage overlap increases, the number of flops in SRE increases because more matrix inversion operations are needed in the implicit LS algorithm.
Although these methods can substantially improve the performance of OFDM systems in high Doppler scenarios with lower complexity, the requirement of the relatively large matrix inversion operations and addition memory is still unavoidable.
This is achieved by applying QL decomposition followed by elementary matrix operations on H in a manner analogous to the modified QL decomposition algorithm based on the Gram-Schmidt orthogonalization procedure, thus avoiding the need for expensive matrix inversion operations.
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The matrix inversion operation of the simplest SMI beamformer has a complexity of O(M3).
The computation of the WF-estimated channel transfer function requires the matrix inversion operation.
Similar to [16], the new iterative update procedure also eliminates the matrix inversion operation to reduce the computational complexity.
Compared with the multistage Wiener filter, it achieves better performance and does not involve matrix inversion operation.
Note that the matrix inversion operation in Equation (16) is transformed into a simple scalar inversion operation in (43) due to the diagonal nature of Ξ ℓ (n).
However, the conventional MC-CDMA system [8] utilizes the MMSE equalizer, which requires a matrix inversion operation, resulting in a computational order of O N3).
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