Iterative-Promoting Variable Step Size Least Mean Square Algorithm for Accelerating Adaptive Channel Estimation

Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergence speed and estimation performance. In this paper, we propose an iteration-promoting variable step size based least-mean-square error (IPVSS-LMS) algorithm to control the convergence speed as well as to improve the estimation performance. Simulation results show that the proposed algorithm can achieve better estimation performance (3dB) than previous ISS-LMS while without sacrificing convergence speed as well as computational complexity.