Training sequence and memory length selection for space-time Viterbi equalization

We consider signal and receiver design for space-time Viterbi equalization for wireless transmission, We propose a search method to find good training sequences, termed min-norm training sequences, for least-square channel es timation. Compared to either a maximum-length sequence ol a randomly genera ted training sequence, the training sequence obtained can drastically reduc e the channel estimation error: We also derive a simple lower bound on the achievable channel estimation error of any training sequence. Knowledge of this lower bound helps the search for min-norm training sequences in that i t facilitates a measure of the goodness of the best sequence examined so fa r, For operation under the situation with unknown channel response lengths, we propose a simple method to select the memory length (tap number) in the Viterbi equalizer based on the SNR of the received signal, The resulting e qualization performance is found to he comparable with the case where a pre set, fixed memory length is used. However, the proposed method often result s in use of a smaller tap number, which translates into a reduction ill the computational complexity, Simulation results show that at symbol error rat e below 10(-2) (SNR > 5 dB) the amount of complexity reduction is of the or der of 5% to 25% on the average, for typical wireless channels.