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Modified givens rotations for inverse updating in qr decomposition

The standard RLS algorithm involves computing matrix inverse at each step which causes numerical instability issues.

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Adaptive equalizers form an inevitable block within receiver’s in general wireless communication systems to reduce the errors caused by intersymbol interference.There are many reasons why a cookie could not be set correctly.Below are the most common reasons: This site uses cookies to improve performance by remembering that you are logged in when you go from page to page.To provide access without cookies would require the site to create a new session for every page you visit, which slows the system down to an unacceptable level.This site stores nothing other than an automatically generated session ID in the cookie; no other information is captured.An error analysis for computing the inverse of a matrix in fixed point environment is done for GS, HH, and GR in our previous work [15].

Of these, Givens rotation has better performance compared to other methods even with lesser number of bits.

In [1], the author discusses different methods based on the dynamic computation of the SNR values to determine the optimum number of filter taps with varying channel conditions.

An implementation of adaptive filtering is complex due to the algorithms involved.

There are different algorithms and architectures proposed in the literature which includes Gram-Schmidt orthogonalization, modified Gram-Schmidt orthogonalization [2, 3], Givens rotation [4–11], householder transformations [12], and various other hybrid methods [13, 14].

Gram-Schmidt (GS) algorithm offers reduced accuracy and stability in fixed precision environment.

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