Blind equalization: a new convex cost function

The use of gradient descent recursive identification in blind adaptive equalization requires a cost function with a unique minimum so that the FIR equalizer setup is guaranteed to remove sufficient ISI. We propose such a cost function which satisfies this requirement. The newly proposed cost function is the difference between the largest (l₁ ) and the smallest (l_∞) norms of the joint channel and equalizer impulse response. The global convergence for arbitrary linear channels holds regardless of the initial ISI and the equalizer length (i.e. for finite length equalizers). A globally convergent blind recursive equalizer is obtained based on an implementable approximation of such a cost function. Computer simulations compare favorably with existing algorithms of a similar kind