An apparatus and method for optimizing the performance of satellite communication system receivers by using the Soft-Input Soft-Output (SISO) BCJR (Bahl, Cocke, Jelinek and Raviv) algorithm to detect a transmitted information sequence is disclosed. A Sliding Window technique is used with a plurality of reduced state sequence estimation (RSSE) equalizers to execute the BCJR algorithm in parallel. A serial data stream is converted into a plurality of data blocks using a serial-to-parallel converter. After processing in parallel by the equalizers, the output blocks are converted back to a serial data stream by a parallel-to-serial converter. A path history is determined using maximum likelihood (ML) path history calculation.

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.

Methods and devices are disclosed for receiving and decoding sparsely encoded data sequences using a message passing algorithm (MPA) or maximum likelihood sequence estimation (MLSE). Such data sequences may be used in wireless communications systems supporting multiple access, such as sparse code multiple access (SCMA) systems. The Methods and devices reduce the number of states in a search space for each received signal and associated function node based on a search threshold based on a characteristic related to the received signal and/or to a quality of a resource element over which the received signal is transmitted.

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.

A sequence detector is provided for detecting symbol values corresponding to a sequence of input samples obtained from a transmission channel. The sequence detector comprises a branch metric unit (BMU), a path metric unit (PMU) and a survivor memory unit. The branch metric unit calculates branch metrics for respective possible transitions between states of a trellis. The path metric unit accumulates branch metrics provided by the branch metric unit in order to establish path metrics. The survivor memory unit selects a survivor path based on the path metrics and outputs a survivor sequence of the detected symbols corresponding to the survivor path. The sequence detector is configured such that the synchronization length is different than the survivor path memory length.

A sequence detector is provided for detecting symbol values corresponding to a sequence of input samples obtained from a transmission channel. The sequence detector comprises a branch metric unit (BMU), a path metric unit (PMU) and a survivor memory unit. The branch metric unit calculates branch metrics for respective possible transitions between states of a trellis. The path metric unit accumulates branch metrics provided by the branch metric unit in order to establish path metrics. The survivor memory unit selects a survivor path based on the path metrics and outputs a survivor sequence of the detected symbols corresponding to the survivor path. The sequence detector is configured such that the synchronization length is different than the survivor path memory length.

A sequence detector is provided for detecting symbol values corresponding to a sequence of input samples obtained from a transmission channel. The sequence detector comprises a branch metric unit (BMU), a path metric unit (PMU) and a survivor memory unit. The branch metric unit calculates branch metrics for respective possible transitions between states of a trellis. The path metric unit accumulates branch metrics provided by the branch metric unit in order to establish path metrics. The survivor memory unit selects a survivor path based on the path metrics and outputs a survivor sequence of the detected symbols corresponding to the survivor path. The sequence detector is configured such that the synchronization length is different than the survivor path memory length.

A sequence detector is provided for detecting symbol values corresponding to a sequence of input samples obtained from a transmission channel. The sequence detector comprises a branch metric unit (BMU), a path metric unit (PMU) and a survivor memory unit. The branch metric unit calculates branch metrics for respective possible transitions between states of a trellis. The path metric unit accumulates branch metrics provided by the branch metric unit in order to establish path metrics. The survivor memory unit selects a survivor path based on the path metrics and outputs a survivor sequence of the detected symbols corresponding to the survivor path. The sequence detector is configured such that the synchronization length is different than the survivor path memory length.

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.