  Telecommunication networks make extensive use of power amplifiers to broaden
the coverage from transmitter to receiver. Achieving high power efficiency is
challenging and comes at a price: the wanted linear performance is degraded due
to nonlinear effects. To compensate for these nonlinear disturbances, existing
techniques compute the pre-inverse of the power amplifier by estimation of a
nonlinear model. However, the extraction of this nonlinear model is involved
and requires advanced system identification techniques.
  We used the plant inversion iterative learning control algorithm to
investigate whether the nonlinear modeling step can be simplified. This paper
introduces the iterative learning control framework for the pre-inverse
estimation and predistortion of power amplifiers. The iterative learning
control algorithm is used to obtain a high quality predistorted input for the
power amplifier under study without requiring a nonlinear model of the power
amplifier. In a second step a nonlinear pre-inverse model of the amplifier is
obtained. Both the nonlinear and memory effects of a power amplifier can be
compensated by this approach. The convergence of the iterative approach, and
the predistortion results are illustrated on a simulation of a Motorola LDMOS
transistor based power amplifier and a measurement example using the Chalmers
RF WebLab measurement setup.
