TY - GEN
T1 - A sparse EM algorithm for blind and semi-blind identification of doubly selective OFDM channels
AU - Barembruch, S.
AU - Moulines, Eric
AU - Scaglione, Anna
PY - 2010/12/1
Y1 - 2010/12/1
N2 - In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the EM algorithm incorporating a sparsity constraint in the maximization step. We apply this algorithm to an OFDM transmission over a doubly-selective multipath channel with strong Doppler and delay spread.
AB - In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the EM algorithm incorporating a sparsity constraint in the maximization step. We apply this algorithm to an OFDM transmission over a doubly-selective multipath channel with strong Doppler and delay spread.
U2 - 10.1109/SPAWC.2010.5670864
DO - 10.1109/SPAWC.2010.5670864
M3 - Conference contribution
AN - SCOPUS:78751512028
SN - 9781424469901
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
T2 - 2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2010
Y2 - 20 June 2010 through 23 June 2010
ER -