Abstract
Up to this point in the text, we have made two key assumptions in discussing the structures described in Chapter 7: we have assumed arbitrary receiver complexity and we have also assumed that the channel characteristics are known at the receiver. For the maximum likelihood sequence estimation receiver, implemented via the Viterbi algorithm, the number of states was allowed to grow without bound and the observables used to compute the transition metrics are outputs of a (presumed known) filter matched to the channel. In the optimum linear receiver, the matched filter appears again along with a tapped delay line equalizer of arbitrary length. In the optimum linear receiver which does not use a matched filter, the tap weights are dependent on the channel covariance matrix. In practice, the channel characteristics are generally not known. If a dialed telephone line is used, the channel is different on each call. Even for private or leased channels, the characteristics may be known only within certain limits. For many channels, such as fading radio systems, phase perturbations and other time-varying channel variations are present, requiring constant tracking to avoid deterioration of performance. The optimum receivers we have described in the preceding chapters would be of academic interest only if it were not possible to adapt the parameters appearing in their structures to accurately model the actual channel or a function of the channel, such as its inverse.
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Gitlin, R.D., Hayes, J.F., Weinstein, S.B. (1992). Automatic and Adaptive Equalization. In: Data Communications Principles. Applications of Communications Theory. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3292-7_8
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DOI: https://doi.org/10.1007/978-1-4615-3292-7_8
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