A decoding method for a convolutionally coded signal is provided. The convolutionally coded signal includes a trellis. The decoding method includes determining a plurality of first sub-trellises from the trellis, decoding the first sub-trellises, determining a plurality of second sub-trellises from the trellis, boundaries of the second sub-trellises being different from boundaries of the first sub-trellises, and decoding the second sub-trellises.

A method for decoding a variable length coded input including a plurality of binary code symbols into an output symbol includes: setting, by a decoder including a processor and memory storing a lookup table including a plurality of states, a current state to an initial state and a current branch length to an initial branch length; and identifying, by the decoder using the lookup table, a next state or a symbol of the output symbols based on a current state, a current branch length, and a next binary code symbol of the variable length coded input.

A read-write method includes: sequentially writing, in a first direction, a code word obtained by information-bit encoding into a target memory cell in each layer of memory cell array in the three-dimensional memory; randomly reading the target memory cell in each layer of memory cell array, or sequentially reading the target memory cell in each layer of memory cell array in a second direction; and determining an LLR value of a current target memory cell according to a storage time corresponding to the current target memory cell when reading, a threshold voltage partition corresponding to the current target memory cell when reading, a comprehensive distribution state corresponding to the current target memory cell when reading, and a pre-established LLR table, so as to perform a soft decoding operation on the code word in the current target memory cell based on the LLR value of the current target memory cell.

A method for decoding a received code using a device that includes: an antenna for receiving a signal over a wireless channel, and instances of a Maximum-A-Posteriori (MAP) turbo decoder for decoding a segment of the received code, are disclosed. For example, the method, by forward and backward gamma engines, for each window, concurrently computes gamma branch metrics in forward and backward directions, respectively, by forward and backward state metric engines comprising respective lambda engines and coupled to the respective gamma engines, for each window, sequentially computes forward and backward state metrics, respectively, based on respective gamma branch metrics and respective initial values, by the lambda engines, determines Log Likelihood Ratios (LLRs) and soft decisions, and by a post-processor, computes extrinsic data based on the forward and backward state metrics for any subsequent iteration as at least a portion of the a-priori information and otherwise provides a decoded segment.

Systems and methods for decoding data transmitted by RFID tags are disclosed. One embodiment of the invention includes an analyzer and equalizer configured to filter an input signal, an estimation block configured to obtain a baseband representation of the modulated data signal by mixing the filtered input signal with the carrier wave, and a coherent detector configured to perform phase and timing recovery on the modulated data signal in the presence of noise and to determine a sequence of data symbols.

An apparatus comprising a memory and a controller. The memory may be configured to store data. The controller may process a plurality of input/output requests to read/write to/from the memory. The controller may generate read data by performing a hard-decision decode on a codeword received from the memory. If the hard-decision decode fails, the controller may enter an error-recovery process comprising a plurality of recovery procedures. At least one of the recovery procedures may apply an inter-cell interference cancellation technique. The error-recovery process may (a) determine parameters for a soft-decision decode by performing one of the recovery procedures on the codeword, (b) execute the soft-decision decode using the parameters from the recovery procedure performed to generate the read data and (c) if the soft-decision decode fails, repeat (a) and (b) using a next one of the recovery procedures.

A memory controller system includes error correction circuitry and erasure decoder circuitry. A retry flow is triggered when the memory controller's error checking and correction (ECC) detects an uncorrectable codeword. Error correction circuitry generates erasure codewords from the codeword with uncorrectable errors. The memory controller computes the syndrome weight of the erasure codewords. For example, the erasure decoder circuitry receives the erasure codewords and computes the syndrome weights. Error correction circuitry orders the erasure codewords based on their corresponding syndrome weights. Then error correction circuitry selects a subset of the codewords, and sends them to erasure decoder circuitry. Erasure decoder circuitry receives the selected codewords and decodes them.

A decoding method for a convolutional code decoding device in a communication system includes receiving convolutional code data, determining the number of times of iteration, and performing an iterative decoding process for the number of times of iteration to decode the convolutional code data.

An apparatus includes a memory and a controller. The memory may be configured to store data. The memory generally comprises a plurality of memory units each having a size less than a total size of the memory. The controller may be configured to generate a set of converted log likelihood ratios by scaling a set of original log likelihood ratios using a selected scalar value, wherein the controller determines the selected scalar value by generating a plurality of sets of scaled log likelihood ratios by scaling the set of original log likelihood ratios with a plurality of corresponding scalar values, calculating a plurality of respective correlation coefficients each measuring a similarity of a respective set of scaled log likelihood ratios to the set of original log likelihood ratios, and selecting the scalar value corresponding to the set of scaled log likelihood ratios whose respective correlation coefficient is highest as the selected scalar value.

RFID data signals from RFID tags may be recovered by determining the probabilities of transitions between data states between a series of a pairs of signal samples using a set of predetermined probabilities related to data, timing, baud rate and/or phase variables affecting the received signal and processing those determined probabilities to determine the sequence of such transitions that has the highest probability of occurrence. A second set of predetermined probabilities related to transitions in the opposite direction may be used to sequence in a reverse direction. The determination of the sequence representing the RFID tag data may be iterated in both directions until further iterations do not change the determined probabilities.