Minimum BER Criterion and Adaptive Moment Estimation Based Enhanced ICA for Wireless Communications

This paper concentrates on investigating an enhanced independent component analysis (ICA) method for blind separation of signals corrupted by noise in wireless communications. Because of the traditional classical ICA methods that always have an inadequate capacity of anti-noise or insufficient separable ability in noise circumstance without satisfying practical application requirements. For this reason, two mechanisms are conducted to establish the modified cost function and fulfill the optimization assignment in the process of constituting an enhanced ICA algorithm. This proposed algorithm can benefit tremendously from the derived minimum bit error rate (BER) criterion and the novel adaptive moment estimation (Adam) optimization approach. In this work, firstly, the novel cost function is obtained according to minimum BER fused into maximum likelihood (ML) principle-based ICA cost function. Furthermore, by utilizing Adam processing, the task of blind separation of mixed signals is implemented via optimizing this modified cost function. Lastly, theoretical analysis and experiment results corroborate the better effectiveness and robustness of the proposed enhanced ICA algorithm compared with a series of popular representative ICA methods.