TY - JOUR
T1 - Training sequence and memory length selection for space-time Viterbi equalization
AU - Chou, Chih Sheng
AU - Lin, David W.
PY - 2000/1/1
Y1 - 2000/1/1
N2 - 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 estimation. Compared to either a maximum-length sequence or a randomly generated training sequence, the training sequence obtained can drastically reduce 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 it facilitates a measure of the goodness of the best sequence examined so far. 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 equalization performance is found to be comparable with the case where a preset, fixed memory length is used. However, the proposed method often results in use of a smaller tap number, which translates into a reduction in the computational complexity. Simulation results show that at symbol error rate below 10-2 (SNR > 5 dB) the amount of complexity reduction is of the order of 5% to 25% on the average, for typical wireless channels.
AB - 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 estimation. Compared to either a maximum-length sequence or a randomly generated training sequence, the training sequence obtained can drastically reduce 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 it facilitates a measure of the goodness of the best sequence examined so far. 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 equalization performance is found to be comparable with the case where a preset, fixed memory length is used. However, the proposed method often results in use of a smaller tap number, which translates into a reduction in the computational complexity. Simulation results show that at symbol error rate below 10-2 (SNR > 5 dB) the amount of complexity reduction is of the order of 5% to 25% on the average, for typical wireless channels.
KW - Channel estimation
KW - Channel length selection
KW - Decision-feedback sequence estimation
KW - Space-time signal processing
KW - Training sequence design
KW - Viterbi equalization
KW - Wireless communication
UR - http://www.scopus.com/inward/record.url?scp=0040431396&partnerID=8YFLogxK
U2 - 10.1109/JCN.2000.6596772
DO - 10.1109/JCN.2000.6596772
M3 - Article
AN - SCOPUS:0040431396
VL - 2
SP - 361
EP - 366
JO - Journal of Communications and Networks
JF - Journal of Communications and Networks
SN - 1229-2370
IS - 4
ER -