Low-Complexity Constrained Constant Modulus SG-based Beamforming Algorithms with Variable Step Size

TL;DR

This paper introduces two low-complexity adaptive step size algorithms for blind beamforming using the constrained constant modulus criterion, demonstrating improved performance and convergence over existing methods.

Contribution

The paper proposes novel stochastic gradient algorithms with variable step size for blind beamforming, enhancing performance and reducing computational complexity.

Findings

Superior performance compared to existing methods

Better convergence behavior in various environments

Lower computational complexity

Abstract

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.