A digital broadcasting system and a method of processing data are disclosed, which are robust to error when mobile service data are transmitted. To this end, additional encoding is performed for the mobile service data, whereby it is possible to strongly cope with fast channel change while giving robustness to the mobile service data.

System and method of timing recovery for recovering a clock signal by using adaptive channel response estimation. The channel response estimation in the timing recovery loop is dynamically adapted to the current channel response that varies over time. More particularly, the channel estimation coefficients used in a channel estimator can be adapted based on an error signal representing the difference between a received signal at the timing recovery loop and an estimated signal output from a channel estimator. Further, to prevent undesirable interaction between the channel estimator and the overall timing recovery loop with respect to clock phase recovery, the adaptation of channel estimation can be controlled in terms of speed or time so as to reduce or eliminate the channel estimator's effect on clock phase correction.

A receiver applies a calibration method to compensate for skew between input channels. The receiver skew is estimated by observing the coefficients of an adaptive equalizer which adjusts the coefficients based on time-varying properties of the multi-channel input signal. The receiver skew is compensated by programming the phase of the sampling clocks for the different channels. Furthermore, during real-time operation of the receiver, channel diagnostics is performed to automatically estimate differential group delay and/or other channel characteristics based on the equalizer coefficients using a frequency averaging or polarization averaging approach. Framer information can furthermore be utilized to estimate differential group delay that is an integer multiple of the symbol rate. Additionally, a DSP reset may be performed when substantial signal degradation is detected based on the channel diagnostics information.

A base station apparatus includes at least one remote unit apparatus including at least one antenna; and a central unit apparatus connected to each remote unit via a transmission path. The remote antenna receives a wireless transmission signal from at least one wireless terminal including at least one antenna. The remote unit includes a channel estimation unit functioning between the terminal antenna and the remote antenna, using a reception signal received by the remote unit antenna; a reception signal processing unit that detects a reception signal corresponding to the transmission signal, using the channel information estimated by the channel estimation unit; a decoding unit that decodes each signal detected by the reception signal processing unit; and an inter-unit transmission unit that transmits each signal decoded by the decoding unit to the central unit. The central unit is provided with an inter-unit receiving unit and a signal selection unit.

An interference processing method comprises: receiving a multi-carrier modulated signal, wherein the multi-carrier modulated signal comprises multi-carrier modulation symbols; discomposing the multi-carrier modulation symbols into a set of subcarriers, wherein the set of subcarriers at least comprises a target subcarrier; equalizing the target subcarrier to obtain an equalized target subcarrier; obtaining an error power of the equalized target subcarrier; and comparing the error power of the equalized target subcarrier with a predefined threshold to determine the existence of interference in the target subcarrier, wherein the predefined threshold is associated with a minimum distance between two constellation points of a modulation constellation of the target subcarrier.

A method for performing blind channel estimation for an MLSE receiver in a communication channel, according to which Initial Metrics Determination Procedure (IMDP) is performed using joint channel and data estimation in a decision directed mode. This is done by generating a bank of initial metrics that assures convergence, based on initial coarse histograms estimation, representing the channel and selecting a first metrics set M from the predefined bank. Then an iterative decoding procedure is activated during which, a plurality of decision-directed adaptation learning loops are carried out to perform an iterative histograms estimation procedure for finely tuning the channel estimation. Data is decoded during each iteration, based on a previous estimation of the channel during the previous iteration. If convergence is achieved, ISI optimization that maximizes the amount of ISI that is compensated by the MLSE is performed.

A base station apparatus includes at least one remote unit apparatus including at least one antenna; and a central unit apparatus connected to each remote unit via a transmission path. The remote antenna receives a wireless transmission signal from at least one wireless terminal including at least one antenna. The remote unit includes a channel estimation unit functioning between the terminal antenna and the remote antenna, using a reception signal received by the remote unit antenna; a reception signal processing unit that detects a reception signal corresponding to the transmission signal, using the channel information estimated by the channel estimation unit; a decoding unit that decodes each signal detected by the reception signal processing unit; and an inter-unit transmission unit that transmits each signal decoded by the decoding unit to the central unit. The central unit is provided with an inter-unit receiving unit and a signal selection unit.

An improved receiver design implements a practical method for modeling users in SIC turbo loop multiuser detection architectures, wherein in each loop unsubtracted estimation errors from previous loops are used to appropriately scale the error covariance matrix for each user, thereby accurately representing the remaining residual interference in the data stream for each desired user. The effect of estimation errors in previous interference cancellation operations is thereby minimized, and symbol estimations in successive turbo loops are improved, for example during multiuser MMSE, multiuser MMSE with interference rejection combining (MMSE-IRC), sample matrix inversion (SMI), or any of their adaptive variants (least mean-square, recursive least square, Kalman filter etc.). The estimated residual symbol energy can be computed per symbol, and then applied to entire data streams, to groups of symbols, or to each symbol separately.

A digital television (DTV) transmitting system is provided that includes an encoder, a group formatter, a packet formatter and a transmission unit. The group formatter forms data groups where the plurality of second known data sequences are spaced 16 segments apart within at least one of the data groups that includes a transmission parameter inserted between the first known data sequence and the plurality of second known data sequences and the first known data sequence has a first M-symbol sequence and a second M-symbol sequence, the first M-symbol sequence and the second M-symbol sequence have a first pattern, each of the plurality of second known data sequences has a second pattern other than the first pattern, and the second pattern is positioned from a last symbol to a previous N symbol in each of the plurality of second known data sequences.

Described is a data processing method and apparatus that aims to estimate an unknown parameter required to improve interference cancellation capability of a maximum likelihood (ML) receiver. The ML receiver may perform channel estimation on a serving cell and an interference cell to obtain a channel estimation matrix. In addition, the ML receiver may calculate first and second metrics for received signals and the channel estimation matrix to determine one or more survivor paths of the serving cell and calculate an Log-Likelihood Ratio (LLR) according to the survivor paths.