A cooperative maximum likelihood MIMO channel estimator

In future wireless telecommunication systems, the needs in terms of capacity will lead the operators to make simultaneous communications in the same time at the same frequency. When the system benefits from diversity, for example channel diversity (spatial separation of emitters) or code diversity (CDMA, space-time coding), the transmission may be assured with reasonable quality. Classical techniques of channel estimation for the single emission case do not offer sufficient performances for the multi-emission case. We propose a cooperative maximum likelihood (ML) channel estimator adapted to the emitted signals in the multi-emitter (multi-user) context. This estimator relies on the hypothesis that the propagation channel is specular. The Cramer Rao bound (CRB) is derived and compared to the performances of the proposed ML estimator. The empirical performances from Monte-Carlo simulations show that this estimator is efficient at high SNR.