Abstract: A method comprises: receiving, from a communication channel, non-binary multilevel symbols that represent corresponding multibit labels each including at least a least-significant bit (LSB) and a most-significant bit (MSB), the non-binary multilevel symbols mapped to the multibit labels according to set-partition labeling, which partitions the non-binary multilevel symbols between a first set and a second set according to a first value and a second value of the LSB, respectively; digitizing the non-binary multilevel symbols to produce symbol samples; and performing Soft-Output-Viterbi (SOV) equalization of the non-binary multilevel symbols based on the symbol samples, to produce decoded symbol information corresponding to the non-binary multilevel symbols.

Abstract: Techniques are presented for mapping a digital data sequence into a signal point sequence for transmission. The signal point sequence belongs to a set of possible signal point sequences. In one example, a digital data sequence is received. Forbidden branch flags that forbid certain signal points in the possible signal points sequences are applied. The signal point sequence is selected from a subset of all the possible signal point sequences based on the digital data sequence. The subset is defined by the forbidden branch flags.

Abstract: Techniques are presented for receiving Quadrature Amplitude Modulated (QAM) symbols from a transmitter via a transmission path. In one example, a demodulator is configured to down-convert an incoming Radio Frequency (RF) signal to a baseband signal and convert the baseband signal to digital samples, and output the digital samples. A demapper is configured to receive the digital samples output from the demodulator and output data encoded in QAM symbols. The demapper is further configured to: determine from a constellation of QAM symbols a subset of QAM symbols that a digital sample from the demodulator may represent; apply an offset to each QAM symbol in the subset of QAM symbols of the constellation to result in a subset of offset QAM symbols; determine which QAM symbol in the subset of offset QAM symbols the digital sample most likely represents; and output data representing a determined QAM symbol.

Abstract: Techniques are presented for mapping a digital data sequence into a signal point sequence for transmission. The signal point sequence belongs to a set of possible signal point sequences. In one example, a digital data sequence is received. Forbidden branch flags that forbid certain signal points in the possible signal points sequences are applied. The signal point sequence is selected from a subset of all the possible signal point sequences based on the digital data sequence. The subset is defined by the forbidden branch flags.

Abstract: Techniques are presented for receiving Quadrature Amplitude Modulated (QAM) symbols from a transmitter via a transmission path. In one example, a demodulator is configured to down-convert an incoming Radio Frequency (RF) signal to a baseband signal and convert the baseband signal to digital samples, and output the digital samples. A demapper is configured to receive the digital samples output from the demodulator and output data encoded in QAM symbols. The demapper is further configured to: determine from a constellation of QAM symbols a subset of QAM symbols that a digital sample from the demodulator may represent; apply an offset to each QAM symbol in the subset of QAM symbols of the constellation to result in a subset of offset QAM symbols; determine which QAM symbol in the subset of offset QAM symbols the digital sample most likely represents; and output data representing a determined QAM symbol.

Abstract: A decoder decodes a soft information input vector represented by an input vector that is binary and that is constructed from the soft information input vector. The decoder stores even parity error vectors that are binary and odd parity error vectors that are binary for L least reliable bits (LRBs) of the input vector. The decoder computes a parity check of the input vector, and selects as error vectors either the even parity error vectors or the odd parity error vectors based at least in part on the parity check. The decoder hard decodes test vectors, representing respective sums of the input vector and respective ones of the error vectors, based on the L LRBs, to produce codewords that are binary for corresponding ones of the test vectors, and metrics associated with the codewords. The decoder updates the soft information input vector based on the codewords and the metrics.

Abstract: A decoder decodes a soft information input vector represented by an input vector that is binary and that is constructed from the soft information input vector. The decoder stores even parity error vectors that are binary and odd parity error vectors that are binary for L least reliable bits (LRBs) of the input vector. The decoder computes a parity check of the input vector, and selects as error vectors either the even parity error vectors or the odd parity error vectors based at least in part on the parity check. The decoder hard decodes test vectors, representing respective sums of the input vector and respective ones of the error vectors, based on the L LRBs, to produce codewords that are binary for corresponding ones of the test vectors, and metrics associated with the codewords. The decoder updates the soft information input vector based on the codewords and the metrics.