In one embodiment, a system provides for optimizing an error rate of data through a communication channel. The system includes a data generator operable to generate a training sequence as a Markov code, and to propagate the training sequence through the communication channel. The system also includes a Soft Output Viterbi Algorithm (SOVA) detector operable to estimate data values of the training sequence after propagation through the communication channel. The system also includes an optimizer operable to compare the estimated data values to the generated training sequence, to determine an error rate based on the comparison, and to change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

There is provided, in accordance with an embodiment, a method of decoding codewords in conjunction with a low-density parity-check (LDPC) code that defines variable nodes and check nodes, the method comprising receiving a codeword over a data channel; evaluating quality of the data channel; and iteratively updating values of the variable nodes to decode the codeword; wherein the values of the variable nodes are updated at different levels of numeric precision depending on the evaluated quality of the data channel.

A radio receiver comprises physical layer circuitry and processor circuitry. The physical layer circuitry receives quadrature amplitude modulation (QAM) symbols via a plurality of subcarriers included in a broadcast radio signal. Each received QAM symbol is a complex symbol comprising multiple bits of encoded source information. The processing circuitry demodulates the received data symbols, generates a constellation sample for each received QAM symbol, generates a soft metric for each bit of encoded information of the received QAM symbols using the constellation sample, and multiplies the soft metric by a channel state information (CSI) weight to produce a Log-likelihood Ratio (LLR) approximation for each bit of encoded information of the received QAM symbols.

Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.

This disclosure relates to data communication networks. An example data communication apparatus includes physical (PHY) layer circuitry that includes transceiver circuitry, decoder circuitry, and a signal analysis unit. The transceiver circuitry receives encoded data symbols via a network link. The received encoded data symbols are encoded using trellis coded modulation (TCM). The decoder circuitry decodes the received encoded data symbols using maximum-likelihood (ML) decoding to map a received symbol sequence to an allowed symbol sequence using a trace-back depth. A trace-back depth value is a number of symbols in the received symbol sequence used by the ML decoding to identify the allowed symbol sequence from the received symbol sequence. The signal analysis unit determines one or more link statistics of the network link, and sets the trace-back depth value according to the one or more link statistics.

This disclosure relates to data communication networks. An example data communication apparatus includes physical (PHY) layer circuitry that includes transceiver circuitry, decoder circuitry, and a signal analysis unit. The transceiver circuitry receives encoded data symbols via a network link. The received encoded data symbols are encoded using trellis coded modulation (TCM). The decoder circuitry decodes the received encoded data symbols using maximum-likelihood (ML) decoding to map a received symbol sequence to an allowed symbol sequence using a trace-back depth. A trace-back depth value is a number of symbols in the received symbol sequence used by the ML decoding to identify the allowed symbol sequence from the received symbol sequence. The signal analysis unit determines one or more link statistics of the network link, and sets the trace-back depth value according to the one or more link statistics.

Systems and methods for decoding block and concatenated codes are provided. These include advanced iterative decoding techniques based on belief propagation algorithms, with particular advantages when applied to codes having higher density parity check matrices such as iterative soft-input soft-output and list decoding of convolutional codes, Reed-Solomon codes and BCH codes. Improvements are also provided for performing channel state information estimation including the use of optimum filter lengths based on channel selectivity and adaptive decision-directed channel estimation. These improvements enhance the performance of various communication systems and consumer electronics. Particular improvements are also provided for decoding HD radio signals, satellite radio signals, digital audio broadcasting (DAB) signals, digital audio broadcasting plus (DAB+) signals, digital video broadcasting-handheld (DVB-H) signals, digital video broadcasting-terrestrial (DVB-T) signals, world space system signals, terrestrial-digital multimedia broadcasting (T-DMB) signals, and China mobile multimedia broadcasting (CMMB) signals. These and other improvements enhance the decoding of different digital signals.

In one embodiment, a system provides for optimizing an error rate of data through a communication channel. The system includes a data generator operable to generate a training sequence as a Markov code, and to propagate the training sequence through the communication channel. The system also includes a Soft Output Viterbi Algorithm (SOVA) detector operable to estimate data values of the training sequence after propagation through the communication channel. The system also includes an optimizer operable to compare the estimated data values to the generated training sequence, to determine an error rate based on the comparison, and to change the training sequence based on the Markov code to lower the error rate of the data through the communication channel.

Examples include techniques for improving low-density parity check decoder performance for a binary asymmetric channel. Examples include logic for execution by circuitry to decode an encoded codeword of data received from a memory 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, decode the encoded codeword using the estimated LLRs to produce a decoded codeword, return the decoded codeword when the decoded codeword is correct, and repeat the decoding using estimated LLRs when the decoded codeword is not correct, up to a second number of times when the decoded codeword is not correct.