Form-hmm, a forward-only realtime modified hidden markov modeling algorithm for tracking bursty digital channels

Bursty communication channels can be modeled as hidden Markov sources where the error source is assumed to exist in one of N states and the source alphabet consists of M symbols. In general the Hidden Markov Model (HMM) of a stationary source is given by P, the N x N state.,transition matrix. The N states are partitioned into M sets such that when the source is in a state belonging to set i, symbol i is generated. HMMs provide great flexibility to the design engineer in interpreting the symbols and the states. In general HMM techniques can be an invaluable tool for channel characterization. The problem with existing techniques is that they are computationally intensive and it is usually impossible to use them in real time applications. In many cases, the existing methods also give non unique models since they tend to use either too many or too few parameters in their model structures. Our main contribution is a highly efficient HMM algorithm that can be used in real time channel tracking applications. The method we use integrates our technique for estimating an equivalent (and in a number of cases, unique) model A with the forward-only procedure developed by Narciso Tan et.al. The resulting algorithm is used on an "improve the model as you go" basis to provide for real time channel tracking. Using empirical modeling techniques for the channel response, we generate observation sequences and then evaluate the performance of our channel tracking algorithm. The algorithm is being implemented on a C40 based development platform to test its real time applicability.