A polarization stream architecture is described. A transmitter may implement a reverse polarization stream to shape a first source signal in a first signal space to a first target signal in a second signal space. The reverse polarization stream is implemented as a cascade of reverse polarization steps. Each reverse polarization step includes a shuffle function, a split function, a scaling function and an offset function. Machine-learning techniques may be used to implement the scaling function and the offset function. A receiver may implement a polarization stream to recover the source signal.

Data from a communications channel is decoded by receiving data bits corresponding to encoded data, determining a set of data representations from the data bits, distributing the set of data representations into bins across a dynamic range to generate a distribution of the data representations, assigning a respective intermediate scale factor to each bin, deriving a set of moments from the intermediate scale factors, combining the moments into a scaling factor, scaling the data representations by the scaling factor, and sending the scaled data representations to a decoder. The data representations may be a histogram or cumulative distribution function of log-likelihood ratios (LLRs) or values based on channel estimates. In an iterative implementation performed until a stopping condition is met, the data representations may be scaled down on later iterations to avoid saturation. A correction factor may be applied to update the scaling factor for later data bits.

A saturation metric that represents a degree of saturation in a low-density parity-check (LDPC) decoding system that uses a fixed-point number representation is determined. The saturation metric is compared against a saturation threshold. In the event the saturation metric exceeds the saturation threshold, at the end of a decoding iteration, a message is more aggressively attenuated compared to when the saturation metric does not exceed the saturation threshold in order to produce an attenuated message. In the event the saturation metric does not exceed the saturation threshold, at the end of the decoding iteration, the message is less aggressively attenuated compared to when the saturation metric does exceed the saturation threshold in order to produce the attenuated message.

A system and method for allocating network resources are disclosed herein. In one embodiment, the system and method are configured to perform: determining a redundancy version and a new data indicator indicated by control information; determining a base graph of a low density parity check code based on which of a plurality of predefined conditions the redundancy version, and/or the new data indicator satisfy; and sending a signal comprising information bits that are encoded based on the determined base graph of the low density parity check code.

A method of generating soft decision detection parameters for a plurality of received signals. The method comprises defining a hard decision boundary and a plurality of quantisation intervals wherein each quantisation interval extends from the hard decision boundary by an interval distance, selecting a log likelihood value from a set of log likelihood values for each received signal based on the quantisation interval in which the received signal is detected, performing a soft decoding using a plurality of log likelihood values, adjusting the set of log likelihood values based on a result of the soft decoding, determining an error probability for a quantisation interval, comparing the error probability against a target error probability and adjusting the interval distance in order to obtain the target error probability.

Data from a communications channel is decoded by receiving data bits corresponding to encoded data, determining a set of data representations from the data bits, distributing the set of data representations into bins across a dynamic range to generate a distribution of the data representations, assigning a respective intermediate scale factor to each bin, deriving a set of moments from the intermediate scale factors, combining the moments into a scaling factor, scaling the data representations by the scaling factor, and sending the scaled data representations to a decoder. The data representations may be a histogram or cumulative distribution function of log-likelihood ratios (LLRs) or values based on channel estimates. In an iterative implementation performed until a stopping condition is met, the data representations may be scaled down on later iterations to avoid saturation. A correction factor may be applied to update the scaling factor for later data bits.

Examples include techniques for improving low-density parity check decoder performance for a binary asymmetric channel in a multi-die scenario. Examples include logic for execution by circuitry to decode an encoded codeword of data received from a memory having a plurality of dies, bits of the encoded codeword stored across the plurality of dies, using predetermined log-likelihood ratios (LLRs) to produce a decoded codeword, return the decoded codeword when the decoded codeword is correct, and repeat the decoding using the predetermined LLRs when the decoded codeword is not correct, up to a first number of times when the decoded codeword is not correct. When a correct decoded codeword is not produced using predetermined LLRs, further logic may be executed to estimate the LLRs for a plurality of buckets of the plurality of dies, normalize magnitudes of the estimated LLRs, decode the encoded codeword using the normalized estimated LLRs to produce a decoded codeword, return the decoded codeword when the decoded codeword is correct, and repeat the decoding using the normalized estimated LLRs when the decoded codeword is not correct, up to a second number of times when the decoded codeword is not correct.

In one implementation, the disclosure provides a system including a detector configured to generate an output of a first log-likelihood ratio for each bit in an input data stream. The system also includes at least one look-up table providing a mapping of the first log-likelihood ratio to a second log-likelihood ratio. The mapping between the first log-likelihood ratio and the second log-likelihood ratio is non-linear. The system also includes a decoder configured to generate an output data stream using the second log-likelihood ratio to generate a value for each bit in the input data stream.

In one implementation, the disclosure provides a system including a detector configured to generate an output of a first log-likelihood ratio for each bit in an input data stream. The system also includes at least one look-up table providing a mapping of the first log-likelihood ratio to a second log-likelihood ratio. The mapping between the first log-likelihood ratio and the second log-likelihood ratio is non-linear. The system also includes a decoder configured to generate an output data stream using the second log-likelihood ratio to generate a value for each bit in the input data stream.

A decoder circuit can include low-density parity-check (LDPC) decoder circuitry having a plurality of stages and an LDPC repository configured to store parity-check information associated with one or more LDPC codes. The LDPC repository is configured to determine a stall requirement for a layer of a first data block and perform a memory check for a second data block. The LDPC repository, in response to the stall requirement indicating a stall for the layer of the first data block and determining that the memory check is satisfied, is further configured to schedule processing of the first data block and the second data block in the LDPC decoder circuitry using the parity-check information by interleaving the layer of the first data block and a layer of the second data block through the plurality of stages of the LDPC decoder circuitry.