The invention discloses a method and a receiving device of the Viterbi algorithm. The method is applicable for a Viterbi decoder that receives an output signal generated by a convolution code encoder processing an original signal. The convolution code encoder includes M registers and M is a positive integer greater than or equal to 2. The method includes the following steps. First, for the first to the Mth data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known M initial values of the M registers. Then, for the Mth-last to the last data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known last M bits values of the original signal, thereby reducing the computational complexity of the add-compare-select unit.

The invention discloses an improved method and a receiving device of the Viterbi algorithm. The improved method is applicable for a Viterbi decoder that receives an output signal generated by a convolution code encoder processing an original signal. The convolution code encoder includes M registers and M is a positive integer greater than or equal to 2. The improved method includes the following steps. First, for the first to the Mth data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known M initial values of the M registers. Then, for the Mth-last to the last data of the output signal, the Viterbi decoder performs the add-compare-select operation based on the known last M bits values of the original signal, thereby reducing the computational complexity of the add-compare-select unit.

Sequence detectors and detection methods are provided for detecting symbol values corresponding to a sequence of input samples obtained from an ISI channel. The sequence detector comprises a branch metric unit (BMU) and a path metric unit (PMU). The BMU, which comprises an initial set of pipeline stages, is adapted to calculate, for each input sample, branch metrics for respective possible transitions between states of a trellis. To calculate these branch metrics, the BMU selects hypothesized input values, each dependent on a possible symbol value for the input sample and L>0 previous symbol values corresponding to possible transitions between states of the trellis. The BMU then calculates differences between the input sample and each hypothesized input value. The BMU compares these differences and selects, as the branch metric for each possible transition, an optimum difference in dependence on a predetermined state in a survivor path through the trellis.

A method for timing recovery for a high-speed data transmission system may be provided. The method comprises receiving an analog input signal at an ADC and passing processed digital signal samples to a Viterbi detector. The method also comprises receiving at least one processed signal sample and at least two sets of at least one candidate symbol each from the Viterbi detector and/or the processed signal samples by timing error detectors and forwarding output digital signals of the timing error detectors via loop filters to related multiplexers. Furthermore, the method comprises selecting one digital signal from each of the multiplexers using a select signal generated by the Viterbi detector, and deriving a control signal controlling a sampling clock of the analog-to-digital converter by at least one of the selected digital signals from the multiplexers.

Sequence detectors and detection methods are provided for detecting symbol values corresponding to a sequence of input samples obtained from an ISI channel. The sequence detector comprises a branch metric unit (BMU) and a path metric unit (PMU). The BMU, which comprises an initial set of pipeline stages, is adapted to calculate, for each input sample, branch metrics for respective possible transitions between states of a trellis. To calculate these branch metrics, the BMU selects hypothesized input values, each dependent on a possible symbol value for the input sample and L>0 previous symbol values corresponding to possible transitions between states of the trellis. The BMU then calculates differences between the input sample and each hypothesized input value. The BMU compares these differences and selects, as the branch metric for each possible transition, an optimum difference in dependence on a predetermined state in a survivor path through the trellis.

Sequence detectors and detection methods are provided for detecting symbol values corresponding to a sequence of input samples obtained from an ISI channel. The sequence detector comprises a branch metric unit (BMU) and a path metric unit (PMU). The BMU, which comprises an initial set of pipeline stages, is adapted to calculate, for each input sample, branch metrics for respective possible transitions between states of a trellis. To calculate these branch metrics, the BMU selects hypothesized input values, each dependent on a possible symbol value for the input sample and L>0 previous symbol values corresponding to possible transitions between states of the trellis. The BMU then calculates differences between the input sample and each hypothesized input value. The BMU compares these differences and selects, as the branch metric for each possible transition, an optimum difference in dependence on a predetermined state in a survivor path through the trellis.

Sequence detectors and detection methods are provided for detecting symbol values corresponding to a sequence of input samples obtained from an ISI channel. The sequence detector comprises a branch metric unit (BMU) and a path metric unit (PMU). The BMU, which comprises an initial set of pipeline stages, is adapted to calculate, for each input sample, branch metrics for respective possible transitions between states of a trellis. To calculate these branch metrics, the BMU selects hypothesized input values, each dependent on a possible symbol value for the input sample and L>0 previous symbol values corresponding to possible transitions between states of the trellis. The BMU then calculates differences between the input sample and each hypothesized input value. The BMU compares these differences and selects, as the branch metric for each possible transition, an optimum difference in dependence on a predetermined state in a survivor path through the trellis.