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sort of method that uses these weight adjustments that could incorporate the iterative detector can

be looked as an iterative least mean square algorithm. In a least mean square algorithm the

equalizer weighs by observing the error between the desired pulse shape and the observed pulse

shape at the equalizer output. This error is based on the observing the sampling instants and then

processing the error to determine the direction that the chip weights should be changed to obtain

the optimum values [27]. Figure 21 below shows a least mean square algorithm for a baseband

adaptive equalizer, that uses a line delay filter similar to that used on the iterative detector in the

SESS system. It assumes that some form of pulse shaping has been utilized in the design, so it

could be used in the SESS system. The equalizer weight adjustments may be achieved by

observing the error between the desired pulse shape and the observed pulse shape at the equalizer

output. There is many different ways that an error can be defined, and there are many papers that

discuss the possibilities.

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Figure 21 - Least Mean Square with Baseband Adaptive Equalization [4]

There has been work done with a similar weighting system in RAKE receivers in [28]. It

concludes that by the maximum-likelihood RAKE receiver limits the effect of pulse jamming by

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