  In this paper, two low-complexity adaptive step size algorithms are
investigated for blind adaptive beamforming. Both of them are used in a
stochastic gradient (SG) algorithm, which employs the constrained constant
modulus (CCM) criterion as the design approach. A brief analysis is given for
illustrating their properties. Simulations are performed to compare the
performances of the novel algorithms with other well-known methods. Results
indicate that the proposed algorithms achieve superior performance, better
convergence behavior and lower computational complexity in both stationary and
non-stationary environments.
