  Least mean square-partial parallel interference cancelation (LMS-PPIC) is a
partial interference cancelation using adaptive multistage structure in which
the normalized least mean square (NLMS) adaptive algorithm is engaged to obtain
the cancelation weights. The performance of the NLMS algorithm is mostly
dependent to its step-size. A fixed and non-optimized step-size causes the
propagation of error from one stage to the next one. When all user channels are
balanced, the unit magnitude is the principal property of the cancelation
weight elements. Based on this fact and using a set of NLMS algorithms with
different step-sizes, the parallel LMS-PPIC (PLMS-PPIC) method is proposed. In
each iteration of the algorithm, the parameter estimate of the NLMS algorithm
is chosen to match the elements' magnitudes of the cancelation weight estimate
with unity. Simulation results are given to compare the performance of our
method with the LMS-PPIC algorithm in three cases: balanced channel, unbalanced
channel and time varying channel.
