Least-squares algorithms for adaptive equalizers

Least-squares algorithms are the fastest converging algorithms for adaptive signal processors, such as adaptive equalizers. The Kalman, fast Kalman, and adaptive lattice algorithms using a least-squares cost function are investigated and extended to complex, fractionally spaced equalizers. It is shown that, for a typical telephone channel, these algorithms converge roughly three times as fast as the conventional stochastic-gradient technique. We analyze and compute the computational complexities and demonstrate that the fast Kalman algorithm is the most efficient in terms of overall performance.