Manufacturing training symbols from future bits: a novel approach to estimate time-varying flat-fading channels

This paper presents a state generated training symbol (SGTS) algorithm as a channel estimation scheme for sequence detector under time-varying flat-fading channels. The basic idea of SGTS is that data-aided unknown parameters estimation can be embedded into the structure of the Viterbi algorithm. By using a systematic convolutional code, the unknown parameter estimation in SGTS will use a future training sequence manufactured by the current state before the actual Viterbi decoding process, which is distinct from the well-known per-survivor processing (PSP) algorithm. Simulation results 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.