RLS CMA blind equalization with adaptive forgetting factor controlled by energy steady state
Ying Xiao, Jin-Yu Sun · 2016
To further improve the performance of recurrent least square (RLS) constant modulus algorithm (CMA) RLS-CMA blind equalization, an RLS-CMA blind equalization algorithm with adaptive forgetting factor controlled by energy steady state was proposed. The cost function of CMA is simplified to meet second normal form, and the blind equalizer can be updated according to RLS algorithm. The energy rate of the blind equalizer is defined, based on which to judge the algorithm enter the steady state. The forgetting factor switches from the small value to big value when the steady state is reached. There are only two forgetting factors can be selected during the iterative process, which can take full advantages of small and big forgetting factors in the RLS algorithm. Computer simulation results show the effectiveness of the proposed algorithm.