A fully pipelined RLS-based array for channel equalization

It is shown that several recursive least squares (RLS) type equalization algorithms such as, e.g., decisiondirected schemes and orthogonalized constant modulus algorithms, possess a common algorithmic structure and are therefore rather straightforwardly implemented on an triangular array (filter structure) for RLS estimation with inverse updating. While the computational complexity for such algorithms isO(N²), whereN is the problem size, the throughput rate for the array implementation isO(1), i.e., independent of the problem size. Such a throughput rate cannot be achieved with standard (Gentleman-Kung-type) RLS/QR-updating arrays because of feedback loops in the computational schemes.