Detection of signals in correlated interference using a predictive VA

We address the problem of optimally detecting signals in correlated noise using a predictive Viterbi algorithm (PVA). We derive expressions for the probability of error for uncoded systems employing the PVA, which are corrupted by coloured noise. As an application, the PVA is used in conjunction with a fractionally-spaced linear equalizer (LE-PVA), thereby improving the bit-error-rate performance by as much as 11 dB, over the conventional decision feedback equalizer with estimated decisions, when the channel has spectral s. Simulation results also show that the performance difference between the LE-PVA and the decision feedback equalizer with correct decisions fed back (ideal DFE), is just 1 dB, even when the channel has spectral s. Thus, we clearly demonstrate the superiority of the LE-PVA over a practical DFE.