A Lower Ordered Hmm Approach to Blind Sequence Estimation

In this paper, the base-band signal collected ~rom an unknown, multipath, multi-receiver FIR channel IS viewed as a state sequence generated by a hidden Markov model (HMM) whose states and order are unknown and whose transition probability matrix with an unknown permutation is known once the order is given. Based on this view, two types of algorithms are developed ,for acquisit~on and tracking, respectively. The algorithms are suitable for both block and non-block transmissions, and for tzme-varying channek. For acquisition, the states and the transition probability matrix of a fully-connected HMM with a poss~ble lower order are estimated by using a clustering algorithm. Then a state sequence is estimated based on a maximum a posterior (MAP) esttmator usang the Vzterbi algorithm with a ,fully-connected trellis. Thzs state sequence is used to refine the estimated states and transdton pro babdit y matmx o,f the HMM. Based on the ,fzdly-connect ed HMM, the states are properly assigned to symbol vectors and the non-, fillyconnected HMM is detemin.ed, whzch is used m the maz~mum kkelzhood (ML) sequence estzmatzon.. In tracking, the symbol sequence zs estzmated by the Viterbi algorzthm, and the states of the HMM are updated at each release o,f estzmated states. Simulation result shows that the proposed blind algorithms with the lower-ordered HMM achieve the performance comparable with the ML estimator utilizing the known channel parameters. Theoretical analysis con@ms simulation results.