Manufacturing training symbols from future bits

This paper presents a state generated training symbol (SGTS) algorithm as a novel channel estimation scheme for sequence detector under time-varying flat-fading channels. The key idea of SGTS is that data-aided unknown parameters estimation can be embedded into the Viterbi decoding structure. By using a systematic convolutional code, the SGTS scheme uses a `future´ training sequence manufactured by the current decoding state to estimate the channel parameter. This is distinct from the conventional per-survivor processing (PSP) algorithm which uses `past´ survivor data to do the estimation. Simulation results are provided to show that the novel SGTS-based sequence detector has similar performance with lower computation load compared with the PSP-based one. Furthermore, SGTS can coordinate with PSP. The resulting sequence detector achieves significant performance improvements with better channel estimation, especially under fast fading channels