Abstract:
Parasitic high-voltage diodes implemented by integration technology in a high-voltage level shift circuit are used for charging a bootstrap capacitor CB, wherein a power supply end of the high voltage level shift circuit is a high-side floating power supply VB, and a reference ground is a floating voltage PGD that is controlled by a bootstrap control circuit. A first parasitic diode DB1 and a second parasitic diode DB2 are provided between the VB and the PGD. The bootstrap control circuit is controlled by a high-side signal and a low-side signal.
Abstract:
Parasitic high-voltage diodes implemented by integration technology in a high-voltage level shift circuit are used for charging a bootstrap capacitor CB, wherein a power supply end of the high voltage level shift circuit is a high-side floating power supply VB, and a reference ground is a floating voltage PGD that is controlled by a bootstrap control circuit. A first parasitic diode DB1 and a second parasitic diode DB2 are provided between the VB and the PGD. The bootstrap control circuit is controlled by a high-side signal and a low-side signal.
Abstract:
The present invention provides a channel estimation method and apparatus based on implicit training sequences for use in wireless communication systems, characterized in that stationary training sequences are superimposed with information sequences for transmission at the transmitting side and channel estimation is performed at the receiving side by using the uncorrelated characteristic between the training sequences and the information sequences, that is, channel estimation is done based on the principle that the estimation result of channel parameter converges to a Wiener solution under the condition that the training sequences and information sequences in the transmission signals are uncorrelated. The method of the invention comprises steps of: obtaining a set of training sequences comprising a plurality of subsets of training sequences, each of the plurality of subsets is generated based on a known initial training sequence and a predetermined channel order; calculating the cross-correlation matrix for the subsets of training sequences and the corresponding inverse matrix; calculating the cross-correlation matrix for the set of training sequences and the received signals; and estimating channel parameters based on the inverse cross-correlation matrix for the subsets of training sequences and the cross-correlation matrix for the set of training sequences and the received signals.