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(b) The lower line is the clock MTIE measurement in the OSA 4520 GPS-SP GPS receiver while the upper line is ITU-T G.811 recommendation.An example of a MTIE measurement of a clock is shown in Figure 4 (recalled from  ) where Figure 4(a) and Figure 4(b) are the TIE and MTIE measurements respectively according to  .
This new tool preserves the short term statistical information unlike the already known tools (BER, ISI, MSE) that lack this information.
Simulation results will show that the equalization performance of a blind adaptive equalizer obtained in the convergence region for two different channels is seen to be approximately the same from the residual ISI and MSE point of view while this is not the case with our new proposed tool.
The paper is organized as follows: after having described the system under consideration in Section II, Section III describes our new proposed tool for analyzing the equalization performance in the convergence region based on the MTIE.
In Section IV simulation results are given using our new proposed tool compared with the existing methods (MSE, ISI) and Section V is our conclusion. System Description In this section we consider the system described in Figure 1 with the following assumptions: 1) The input sequence is the Kronecker delta function) and the noise error passed via the filter (equalizer).
Namely, in the short term, there may be seen different amounts of errors for the two different channels. Therefore, the following question may arise: is it possible to get also short term statistical information of the blind adaptive equalization performance in the convergence region?
A major topic of discussion in standard bodies dealing with network synchronization    is clock noise characterization and measurement  .An example for a MCon E measurement belonging to an equalization process in the convergence state is shown in Figure 6 where Figure 6(a) and Figure 6(b) are the Con E (6) and MCon E (7) measurements respectively.The resemblance between the example in Figure 4 and the example in Figure 6 is due to the nature of the time error (Figure 4(a)) and the error for the TE and Con E calculations respectively). Simulations Results In this section we present several simulation results using the MCon E tool for obtaining the blind adaptive equalization performance using Godard’s algorithm  with a 16QAM input sequence for , compared to the existing methods (ISI and MSE).Please note that according to Figure 4(b) the clock has less time errors for small intervals while the time error increases for bigger intervals i.e.the clock has less time errors in short term and the time error increases in long term.The purpose of this work is to provide an additional tool (additional to the ISI, MSE and BER) for diagnosing equalization performance in the steady state region based on the MTIE method used in the telecommunication area.Simulation results will show that our new proposed tool provides us short term as well as long term statistical information and is able to show differences in the equalization performance comparison obtained in the convergence state even when it is quite difficult to see it with the MSE and ISI method.Blind equalization algorithms are essentially adaptive filtering algorithms designed such that they do not require the external supply of a desired response to generate the error signal in the output of the adaptive equalization filter  .The algorithm itself generates an estimate of the desired response by applying a nonlinear transformation to sequences involved in the adaptation process  .Thus, our new proposed tool might be considered as a more sensitive tool compared to the ISI and MSE method. Introduction In data communication, signals transmitted between remote locations often encounter a signal-altering physical channel (in wired communications or in wireless communications).These physical channels may cause signal distortion, including echoes and frequency-selective filtering of the transmitted signal  .