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In our work, we develop and study successive optimization Tomlinson-Harashima precoding (SO-THP) algorithms based on the generalized matrix inversion approach reported in [11].
For the matrix inversion approach, in theory the optimal delay δ2 should be equal to the number of zeros outside the unit circle, in this example given by δ2 = 99.
The approach taken is to compute the forced response patterns of various idealised systems, and from these to calculate the parameters of Statistical Energy Analysis model for the systems using the matrix inversion approach [1].
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Two incremental approaches are introduced to solve the RBF system of equations: 1) block matrix inversion based approach and 2) modified LU decomposition approach.
For matrix inversion, the approach given in [22] is followed.
The minimum mean square error estimation (MMSE) criterion can be used to estimate the desired user propagation channel, while the covariance matrix can be estimated by the sample matrix inversion (SMI) approach [7 9].
Using a five-tap filter also avoids matrix inversion if the approach mentioned in the paper is followed.
The proposed approach avoids matrix inversion and allows subsets to overlap, thus achieving better diversity gain.
Meanwhile, CSM-based precoding scheme is able to achieve the near-optimal performance by decomposition and iteratively approach the exact matrix inversion of large size in RZF precoding or MMSE precoding.
Although the complexity of the aforementioned approaches based on matrix inversion can be further reduced if specific properties of the signal model are exploited [15, 22, 23], it still remains considerable.
From matrix inversion lemma [33], the inverse matrix of J is: (21).
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