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Efficiently computed reduced-parameter input-aided MMSE equalizers for ML detection: a unified approach

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2 Author(s)
Al-Dhahir, N. ; Inf. Syst. Lab., Stanford Univ., CA, USA ; Cioffi, J.M.

A unified approach for computing the optimum settings of a length-Nf input-aided equalizer that minimizes the mean-square error between the equalized channel impulse response and a target impulse response of a given length Nb is presented. This approach offers more insight into the problem, easily accommodates correlation in the input and noise sequences, leads to significant computational savings, and allows us to analyze a variety of constraints on the target impulse response besides the standard unit-tap constraint. In particular, we show that imposing a unit-energy constraint results in a lower mean-square error at a comparable computational complexity. Furthermore, we show that, under the assumed constraint of finite-length filters, the relative delay between the equalizer and the target impulse response plays a crucial role in optimizing performance. We describe a new characterization of the optimum delay and show how to compute it. Finally, we derive reduced-parameter pole-zero models of the equalizer that achieve the high performance of a long all-zero equalizer at a much lower implementation cost

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Information Theory, IEEE Transactions on  (Volume:42 ,  Issue: 3 )