Sentence examples for data equalization from inspiring English sources

The upcoming data equalization is performed by multiplying the inverse of the channel estimation matrix obtained in the first step with the received data symbol.

During this process, the pilot symbols are inserted in parallel and at the receiving end they are used for calculating H matrix which consists of channel estimates for different subcarrier positions and is later used for data equalization.

The estimated CIR is then, translated into the frequency domain by means of FFT which results in an estimation of the CFR that can be used for data equalization process.

The decimated samples are input to an OFDM receiver that performs frame detection, and time and fractional carrier frequency offset (CFO) estimation before the DFT; and channel estimation, ICI cancellation, data subcarriers equalization, and channel decoding after the DFT. Figure 2 shows the general structure of the receiver.

As presented in [23 26], the iterative channel estimation methods are combined with equalization, data detection, decoding, or even interference cancellation.

At higher data rates, many equalization techniques will be used to compensate for jitter and noise from transmitter and channel so that an "open eye" can be achieved before the receiver sampling flop to warrant the required BER.

QAM and adaptive equalization permitted data transmission of 9.6 kilobits per second over four-wire circuits.

At the end of the training phase, the receiving node has imperfect CSI for channel equalization and data detection.

It reviews the modulation and coding techniques used for data transmission, and the equalization techniques used at the receiver to overcome signal dispersion, and the issues of parameter estimation including the carrier phase, symbol timing recovery, and the channel estimation.

According to these conclusions, an entire 2×× 2 HF-MIMO system has been developed with compact antenna arrays at both ends and standard solutions for signal processing: transmission of data blocks, frequency domain equalization, and periodically refreshed channel estimation using training sequences.

The Weka algorithm used for filtering with Unsupervised discretisation involves separating the data in ranges using equal-frequency binning (histogram equalization) so that the same number of training example fall into each bin.