Implicitly-trained channel estimation and equalization with zero mean input data packets

An Improved Channel Estimation Algorithm Based on Superimposed Training Sequence

“…Finally, the simulation of the Cramer-Rao bound and the cumulative distribution of channel capacity of mean square error is given, and compare it with Rayleigh fading channel. The channel capacity can not be increased and the Cramer-Rao bound can not obtain the minimal loss of system performance with the inappropriate number of antenna, which is consistent with the theoretical analysis results [5,7,12].…”

“…At present, a lot of papers on MIMO system's performance analysis, space-time decoding and correlate detection of Signal, have remained at the assumption that the state information of channel(mainly refers to channel propagation parameters) is known, that is ideal channel situation. In fact, the channel state information is usually obtained from channel estimation by sending the training sequence [1,2] [ [5][6][7] or inserting the pilot signal [10] . Using this method for channel estimation, training sequence should be allocated the time slot specifically, in the context of the loss of bandwidth, the channel coefficients could be estimated.…”

“…LS estimation algorithm is a general algorithm with simple structure and low computational complexity. In addition, channel estimation methods include blind channel estimation and semi-blind estimation [4,5,7] and try to avoid the use of training sequence, thus the information transmission rate is improved effectively, but the blind estimation that based on higher-order statistics takes a long time to observe the data, not only the high computational complexity that go against the real-time estimate, but it is unrealistic to assume that the channel keeps constant in a long data segment. Estimation error will affect the performance of the system no matter what algorithm applied to channel estimation [3,4,8,9] .…”

Channel estimation and symbol detection for block transmission using data-dependent superimposed training

“…Recent contributions are based on superimposing a known periodic sequence on the data [3]- [7]. In [7], the deterministic (or sample) mean of each data block was removed prior to transmission, and this approach was shown to reduce the effect of the unknown data on the performance of both channel estimation and equalization. Here, we propose an ST scheme that fully cancels the effects of the unknown data on the performance of the channel estimator.…”