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SUMMARYThe constant modulus adaptive blind equalization algorithms presented in this paper are shown to correspond to an error performance surface which is much improved upon that of existing algorithms such as the wellknown constant modulus (or Godard) algorithm. Many undesirable local solutions (ULSs) are avoided by careful derivation. We use a deterministic optimization criterion with a soft constraint to obtain an update equation which contains a normalized gradient vector and a particular continuous non-linearity. This approach is extended to multiple constraints to yield faster converging algorithms. An autoregressive (AR) channel model is studied to demonstrate analytically the absence of a class of ULSs. Finally, the ÿndings are veriÿed experimentally for various AR and moving-average (MA) channels. ? 1998 John Wiley & Sons, Ltd.

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