The present invention is directed to data communication. More specifically, an embodiment of the present invention provides an error correction system. Input data signals are processed by a feedforward equalization module and a decision feedback back equalization module. Decisions generated by the decision feedback equalization module are processed by an error detection module, which determines error events associated with the decisions. The error detection module implements a reduced state trellis path. There are other embodiments as well.

The present invention is directed to data communication. More specifically, an embodiment of the present invention provides an error correction system. Input data signals are processed by a feedforward equalization module and a decision feedback back equalization module. Decisions generated by the decision feedback equalization module are processed by an error detection module, which determines error events associated with the decisions. The error detection module implements a reduced state trellis path. There are other embodiments as well.

Certain aspects of the present disclosure generally relate to techniques for efficient, high-performance decoding of low-density parity check (LDPC) codes, for example, by using an adjusted minimum-sum (AdjMS) algorithm, which involves approximating an update function and determining magnitudes of outgoing log likelihood ratios (LLRs). Similar techniques may also be used for decoding turbo codes. Other aspects, embodiments, and features (such as encoding technique) are also claimed and described.

The present invention is directed to data communication. More specifically, an embodiment of the present invention provides an error correction system. Input data signals are processed by a feedforward equalization module and a decision feedback back equalization module. Decisions generated by the decision feedback equalization module are processed by an error detection module, which determines error events associated with the decisions. The error detection module implements a reduced state trellis path. There are other embodiments as well.

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 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 communication system includes a data source to receive a block of bits, a memory to store a set of distribution matchers. Each distribution matcher is associated with a probability mass function (PMF) to match equally likely input bits to a fixed number of output bits with values distributed according to the PMF of the distribution matcher. Each distribution matcher is associated with a selection probability, such that a sum of joint probabilities of all distribution matchers equals a target PMF. A joint probability of a distribution matcher is a product of PMF of the distribution matcher with the selection probability of the distribution matcher. The communication system also includes a shaping mapper to select the distribution matcher from the set of distribution matchers with the selection probability and to map the block of bits to a block of shaped bits with a non-uniform distribution using the selected distribution matcher and a transmitter front end to transmit the block of shaped bits over a communication channel, such that bits in a sequence of the blocks of shaped bits are distributed according to the target PMF.

Certain aspects of the present disclosure generally relate to techniques for efficient, high-performance decoding of low-density parity check (LDPC) codes, for example, by using an adjusted minimum-sum (AdjMS) algorithm, which involves approximating an update function and determining magnitudes of outgoing log likelihood ratios (LLRs). Similar techniques may also be used for decoding turbo codes. Other aspects, embodiments, and features (such as encoding technique) are also claimed and described.

Provided are an electronic device capable of learning a log likelihood ratio (LLR) scaling factor distribution, and an operation method of the electronic device. The operation method of the electronic device includes receiving training environment information, obtaining a measurement log likelihood ratio (LLR) distribution, based on the training environment information, obtaining an inter-distribution divergence value, based on a reference LLR distribution and the measurement LLR distribution, and obtaining an LLR scaling factor distribution by converting the inter-distribution divergence value into a probability value.