Error correcting least-squares Subspace algorithm for blind identification and equalization

Abstract A subspace based blind channel identification algorithm using only the fact that the received signal can be oversampled is proposed. No direct use is made in this algorithm of either the statistics of the input sequence or even of the fact that the symbols are from a finite set and therefore this algorithm can be used to identify even channels in which arbitrary symbols are sent. Using this algorithm as a base and using the extra information which becomes available when the transmitted symbols are from a known finite set, the EC-LS-Subspace algorithm is derived. The EC-LS-Subspace algorithm operates directly on the data domain and therefore avoids the problems associated with other algorithms which use the statistical information contained in the received signal directly. In the noiseless case, if some conditions are met, it is possible for the proposed Basic Subspace algorithm to identify the channel exactly using an observation interval of just (J+2)T, if the length of the impulse response of a channel is JT,T being the symbol interval. In the noisy case, simulations have shown that the channel can be identified accurately by using a very small observation interval (comparable to (J+2)T).