Blind multiuser detection using hidden Markov models theory

We present an adaptive algorithm based on the theory of hidden Markov models (HMM) which is capable of jointly detecting the users in a DS-CDMA system. The proposed technique is near-far resistant and completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. In addition to this, an estimate of the signature of each user convolved with its physical channel impulse response (CIR), and an estimate of the background noise variance are provided once convergence is achieved (as well as estimated data sequences). At this moment, and using that CIR estimate, we can switch to any decision-directed (DD) adaptation scheme.