Blind and semi-blind maximum likelihood techniques for multiuser multichannel identification

We investigate blind and semi-blind maximum likelihood techniques for multiuser multichannel identification. Two blind Deterministic ML methods based on cyclic prediction filters are presented [1]. The Iterative Quadratic ML (IQML)algorithm is used in [1] to solve it: this strategy does not perform well at low SNR and gives biased estimates due to the presence of noise. We propose a modification of IQML we call DIQML to "denoise" it and explore a second strategy called Pseudo-Quadratic ML (PQML). As proposed in [2], PQML works well only at very high SNR. The solu-tion we present here makes it work well at rather low SNR conditions and outperform DIQML. Like DIQML, PQML is proved to be consistent, asymptotically insensitive to the ini-tialisation and globally convergent. Furthermore, it has the performance as DML. A semi-blind extension com-bining these algorithms with training sequence based ap-proaches is also studied. Simulations will illustrate the per-formance of the different algorithms which are found to be close to the Cramer-Rao bounds.