Enhanced joint data detection and turbo map channel estimation using randomly rotated constellations

In this paper, we propose a joint data detection and a turbo maximum-a-posteriori (MAP) time-varying channel estimation in Slotted ALOHA MIMO systems using rotated constellations diversity. Our main idea is to use a randomly rotated and unrotated constellation, together with coding and interleaving, for each user in order to increase the diversity order and to improve the collision resolution at the receiver side. The burst-by-burst turbo-MAP channel estimator proposed is based on Space Alternating Expectation Maximization (SAGE) algorithm. Our proposed approach allows an efficient separation of colliding packets even if they are received with equal powers. Simulation results are given to support our claims.