Doubly-Selective Multiuser Channel Estimation using Superimposed Training and Discrete Prolate Spheroidal Basis Expansion Models

Channel estimation for multiuser doubly-selective channels is considered using superimposed training. The time-varying channel is assumed to be described by a discrete prolate spheroidal basis expansion model (DPS-BEM). A user-specific periodic training sequence is arithmetically added (superimposed) at a low power to each user´s information sequence at the transmitter before modulation and transmission. A two step approach is adopted where in the first step we estimate the channel using only the first-order statistics of the observations. Using the estimated channel from the first step, a Viterbi detector is used to estimate the information sequence. In the second step a deterministic maximum likelihood (DML) approach is used to iteratively estimate the multiuser channel and the information sequences sequentially.