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.

Advanced ultra-low power error correcting codes are generated using soft quantization and lattice interpolation based on clock and Syndrome Weight. Reinforcement learning may be used to generate threshold values for flipping bits for low density parity check Ultra-Low Power error correction codes. The threshold values can be generated offline and downloaded to a storage device or generated while the storage device is in use.

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.

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.

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.

The invention relates to a MAP decoding method of a signal received through a noisy channel, the signal being composed of symbols in an alphabet having a non-uniform probability distribution, the symbols being represented by points in a lattice (). The probability distribution of symbols is modeled using a Gaussian distribution. An augmented lattice (.sub.exp) is formed from the lattice () and the ratio () between variance of the noise and variance of the Gaussian distribution of symbols. Therefore, the disclosed MAP decoding method consists essentially of decoding using an ML criterion searching the point in the augmented lattice closest to the point representative of the received signal (y.sub.exp).

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.