Block Amari Alpha Least Mean Square Algorithm based Channel Estimation for MIMO Network

With the increase in the number of users, the channel estimation in the presence of pilot contamination has become more challenging. To reduce the computational complexity involved in performing adaptive channel estimation in real time, block adaptive filters are widely used. This paper proposes a channel estimation technique that uses least mean square (LMS) adaptive filtering algorithm based on information-theoretic divergence, named Amari-Alpha divergence based Block LMS (AABLMS) algorithm. This algorithm is used to study a scenario where multiple users receive contaminated pilot signals. The condition for convergence of the proposed AABLMS algorithm in the mean sense is derived, and the upper and lower bounds for the learning rate are derived. Further, the block counterparts of existing state-of-the-art LMS variants are compared with that of the proposed AABLMS algorithm in terms of computational complexity, mean square deviation (MSD), and mean square error (MSE). The simulation results show that the proposed AABLMS algorithm performs better than other block LMS-based counterparts in the presence of channel noise and pilot contaminating noise.