Many existing multiuser detection algorithms assume that the user sequences are independent and identically distributed (i.i.d.). These algorithms, however, may not be efficient when the user sequences sent to a multiuser system are time correlated due to signal processing procedures such as channel coding. In this paper, we assume that the user sequences are time correlated and can be modeled as first-order, finite-state Markov chains. The proposed algorithm applies the decision feedback framework in which a linear filter based on the maximum target likelihood (MTL) criterion is derived to remove the interferences. A hidden Markov model (HMM) estimator is applied to the output of the MTL filter to estimate the user data, noise variance, and state transition probabilities. The estimated user data in turn are applied to update the parameters of the MTL filter. By exploiting the transmission of training symbols, the proposed algorithm requires neither knowledge of the user codes nor the timing information. Simulation results show the performance improvement of the proposed algorithm by exploiting the time-correlated redundancy of the Markov sources. © 2009 IEEE.
Bibliographical noteThis work was supported in part by the National Science Foundation of China under Grant 60402014 and Grant 60672065 and in part by the City University of Hong Kong under Grant 7002073.
- Code-division multiaccess (CDMA)
- Hidden Markov models (HMMs)
- Interference suppression
- Maximum likelihood (ML) estimation