The central theme of this research is the development of a fuller theoretical description of the behavior of memoryless-error- function (or Bussgang) type blind equalization algorithms, in particular CMA, as used in practice with finite-length, fractional spacing, higher-order (non-constant modulus, non-uniformly distributed) source constellations, (deterministic and stochastic) correlated source time series, and modest channel noise. The initial target is the behavior of fractionally-spaced CMA with a correlated (or periodic) source, which appears capable of causing misbehavior in practice. We plan to use analytical tools from topologically based, algebraic-geometry (in particular Bernstein's theorem, relaxation methods, and Morse theory) non typically associated with the study of adaptive filters in addition to averaging theory tools popular in adaptive systems analysis. The ultimate intent is to convert an expanded theory into situation dependent design guidelines for stepsize, length, fractional- spacing, initialization selection, and failure recovery tricks for the use of CMA as a start-up scheme (with a subsequent switch to decision-direction) for high data throughput applications. We plan to translate our analytical advances on CMA to fractionally-spaced realizations of other blind equalizer algorithms of the memoryless error function class (including refinements to CMA), for which actual operating data can be obtained industrially as for CMA. We also plan to use our behavior insights into CMA to help provide fair comparisons on actual operating data to competing schemes such as symbol-spaced decision feedback equalizers and similar complexity algorithms based on second-order correlation statistics of single-input, multiple- output models of fractionally-spaced equalization.
