Theoretical analysis shows that the proposed algorithm provides a potential solution to suppress the co-channel interference with low complexity and reduce the computation complexity without adding extra interference suppression matrices and computation complexity at receivers.
The separable compressive sensing theory uses two-dimensional separable random measurement matrixes instead of a huge size of random matrix to remedy sensing matrix storage and computation complexity.
This paper combines the spectral clustering with MapReduce, through evaluation of sparse matrix eigenvalue and computation of distributed cluster, puts forward the improvement ideas and concrete realization, and thus improves the clustering speed of the distinctive clustering algorithm.
To overcome the difficulty, a separable compressive sensing operator was proposed and the cost of it manifested as a small increase of the number of compressive samples but remedied sensing matrix storage and computation complexity [7, 8].
From a given frame of EEG data, reflection coefficients are directly extracted by using the autocorrelation values in a recursive fashion, which avoids matrix inversion and computation of AR parameters.
In our biquaternion beamspace, the memory spaces for storing the data covariance matrix and the computation efforts for performing its EVD are reduced.
