Blind source separation of signals with known alphabets using (epsilon)-approximation algorithms

We show that blind separation of signals in given alphabets can be formulated into a quadratic optimization problem with integer constraints. Then, efficient (epsilon)-approximation algorithms are applied to directly estimate the transmitted signals. The proposed approach does not require any high order statistics. Moreover, the algorithms converge to an (epsilon) neighborhood of the global optimum with polynomial computational complexity. Simulations show that the algorithm achieves satisfactory performance using a short length of data.