Methods and apparatuses are provided for operating a list Viterbi decoder. A path metric difference (PMD) threshold is set based on an input signal level and a PMD limit value. Decoding is performed by using the PMD threshold. Performing the decoding includes determining a PMD of a best path, comparing the determined PMD and the PMD threshold, and declaring a decoding failure and ending performing of the decoding, if the PMD is greater than or equal to the PMD threshold.

Certain aspects of the present disclosure relate to techniques and apparatus for increasing decoding performance and/or reducing decoding complexity. An exemplary method generally includes receiving, via a wireless medium, a codeword encoded using a tailless convolutional code (TLCC) with a known start state, evaluating a set of decoding candidate paths through a trellis decoder that originate at the known start state of the TLCC, performing, for each of a plurality of the decoding candidate paths, a back trace from a respective end state to the known start state, and selecting one of the decoding candidate paths based, at least in part, on path metrics generated while performing the back trace. Other aspects, embodiments, and features are also claimed and described.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may receive a data packet that includes at least in part a preamble, an encoded block, and a payload. The apparatus may detect the preamble of the data packet at a last symbol of the preamble or prior to the last symbol of the preamble. The apparatus may compute a branch metric for each of a plurality of transitions between states. The apparatus may initialize a path metric for each of a plurality of non-synchronization states and synchronization states. In certain aspects, each of the synchronization states may be associated with the preamble. The apparatus may determine a survivor path for each of the non-synchronization states and synchronization states based at least in part on a respective path metric. The apparatus may determine a traceback timing.

A method includes: generating a trellis; generating one or more predicted symbols using a first non-linear model; computing and saving two or more branch metrics using a priori log-likelihood ratio (LLR) information, a channel observation, and the one or more predicted symbols; if alpha forward recursion has not yet completed, generating alpha forward recursion state metrics using a second non-linear model; if beta backward recursion has not yet completed, generating beta backward recursion state metrics using a third non-linear model; if sigma forward recursion has not yet completed, generating sigma forward recursion state metrics using the branch metrics, the alpha state metrics, and the beta backward recursion state metrics; generating extrinsic information comprising a difference of a posteriori LLR information and the a priori LLR information; computing and feeding back the a priori LLR information; and calculating the a posteriori LLR information.

A convolutional decoder includes a first storage, a second storage, a branch metric processor to determine branch metrics for transitions of states from a start step to a last step according to input bit streams, an ACS processor to select maximum likelihood path metrics to determine a survival path according to the branch metrics and to update states of the start step to the first storage and the second storage alternately based on the selection of the maximum likelihood path metrics, and a trace back logic to selectively trace back the survival path based on the states of the start step stored in a selected storage among the first storage and the second storage.

A method includes: generating a trellis; generating one or more predicted symbols using a first non-linear model; computing and saving two or more branch metrics using a priori log-likelihood ratio (LLR) information, a channel observation, and the one or more predicted symbols; if alpha forward recursion has not yet completed, generating alpha forward recursion state metrics using a second non-linear model; if beta backward recursion has not yet completed, generating beta backward recursion state metrics using a third non-linear model; if sigma forward recursion has not yet completed, generating sigma forward recursion state metrics using the branch metrics, the alpha state metrics, and the beta backward recursion state metrics; generating extrinsic information comprising a difference of a posteriori LLR information and the a priori LLR information; computing and feeding back the a priori LLR information; and calculating the a posteriori LLR information.

This disclosure relates to data communication networks. An example data communication apparatus includes physical (PHY) layer circuitry that includes transceiver circuitry, decoder circuitry, and a signal analysis unit. The transceiver circuitry receives encoded data symbols via a network link. The received encoded data symbols are encoded using trellis coded modulation (TCM). The decoder circuitry decodes the received encoded data symbols using maximum-likelihood (ML) decoding to map a received symbol sequence to an allowed symbol sequence using a trace-back depth. A trace-back depth value is a number of symbols in the received symbol sequence used by the ML decoding to identify the allowed symbol sequence from the received symbol sequence. The signal analysis unit determines one or more link statistics of the network link, and sets the trace-back depth value according to the one or more link statistics.

This disclosure relates to data communication networks. An example data communication apparatus includes physical (PHY) layer circuitry that includes transceiver circuitry, decoder circuitry, and a signal analysis unit. The transceiver circuitry receives encoded data symbols via a network link. The received encoded data symbols are encoded using trellis coded modulation (TCM). The decoder circuitry decodes the received encoded data symbols using maximum-likelihood (ML) decoding to map a received symbol sequence to an allowed symbol sequence using a trace-back depth. A trace-back depth value is a number of symbols in the received symbol sequence used by the ML decoding to identify the allowed symbol sequence from the received symbol sequence. The signal analysis unit determines one or more link statistics of the network link, and sets the trace-back depth value according to the one or more link statistics.

The present invention relates to a method and apparatus for channel estimation between a transmitter and a receiver in a wireless communications system. In one arrangement, the method comprises: receiving at the receiver a first sequence of bits representing a first sequence of coded symbols transmitted over the communications channel; decoding the first sequence of coded symbols using maximum-likelihood based decoding including: generating traceback outcomes by tracing backwards the first sequence of bits through a maximum-likelihood based traceback path, the traceback outcomes including a first portion associated with a first traceback depth and a second portion associated with a second traceback depth that is deeper than the first traceback depth; generating a channel estimate of the communications channel based on the first portion of the traceback outcomes; and generating an estimate of at least some information bits coded in the first sequence of coded symbols based on the second portion of the traceback outcomes.

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.