Agere Systems Inc.

Lucent Technologies Inc.

Agere Systems Guardian Corp.

Inkyu Lee

Jeffrey Lee Sonntag

Carl-Erik Wilhelm Sundberg

Albert Chan

Thaddeus John Gabara

Syed Aon Mujtaba

Bane Vasic

Bane V Vasic

Marissa L Lopez-Vallejo

A system employs space-time coding characterized at the transmitter by bit-interleaved coded modulation (BICM) combined with multi-carrier Orthogonal Frequency Division Multiplexing (OFDM) modulation. Space-Time coding techniques improve transmission efficiency in radio channels by using multiple transmit and/or receive antennas and coordination of the signaling over these antennas. Bit-interleaved coded modulation provides good diversity gain with higher-order modulation schemes that employ binary convolutional codes. OFDM modulation allows for wideband transmission over frequency selective radio channels. A receiver demodulates the OFDM signal and applies multi-input, multi-output (MIMO) demapping to estimate the BICM encoded bitstream. After deinterleaving of the BICM encoded bitstream, maximum a posteriori (MAP) decoding is applied to the resulting bit stream to generate soft output values. The MIMO demapping and MAP decoding processes exchange likelihood information to improve the bit error rate performance over several iterations of demapping/decoding. By applying well-known turbo-decoding principles to iteratively demap and decode, the overall receiver performance is significantly improved.

Space-time bit-interleaved coded modulation for wideband transmission

A maximum a posteriori (MAP) processor employs a block processing technique for the MAP algorithm to provide a parallel architecture that allows for multiple word memory read/write processing and voltage scaling of a given circuit implementation. The block processing technique forms a merged trellis with states having modified branch inputs to provide the parallel structure. When block processing occurs, the trellis may be modified to show transitions from the oldest state at time k&#x2212;N to the present state at time k. For the merged trellis, the number of states remains the same, but each state receives 2input transitions instead of the two input transitions. Branch metrics associated with the transitions in the merged trellis are cumulative, and are employed for the update process of forward and backward probabilities by the MAP algorithm. During the update process, the read/write operation for an implementation transfers N words of length N for each update operation, but the frequency (and hence, number) of update operations is reduced by a factor of N. Such voltage scaling and multiple word memory read/write may provide reduced power consumption for a given implementation of MAP processor in, for example, a DSP.

Block processing in a maximum a posteriori processor for reduced power consumption

A system employs space-time coding characterized at the transmitter by bit-interleaved coded modulation (BICM) combined with modulating several streams of the BICM encoded data for transmission over two or more antennas. Space-time coding techniques improve transmission efficiency in radio channels by using multiple transmit and/or receive antennas and coordination of the signaling over these antennas. Bit-interleaved coded modulation provides good diversity gain with higher-order modulation schemes that employ binary convolutional codes. A receiver demodulates the received signals and applies multi-input, multi-output (MIMO) demapping to estimate the BICM encoded bitstream. After deinterleaving of the BICM encoded bitstream, maximum a posteriori (MAP) decoding is applied to the resulting bit stream to generate soft output values. By applying well-known turbo-decoding principles to iteratively demap and decode, the overall receiver performance is significantly improved. The MIMO demapping and MAP decoding processes exchange likelihood information to improve the bit error rate performance over several iterations of demapping/decoding. By generating tentative decisions for transmitted bits, the overall number of evaluations used for demapping may be reduced.

Reduced complexity receiver for space-time- bit-interleaved coded modulation

A maximum a posteriori (MAP) detector/decoder employs an algorithm that computes log-likelihood value with an a posteriori probability (APP) value employing a number N of previous state sequences greater than or equal to two (N&gE;2). By defining the APP with more previous state sequences, the set of &agr; values may be calculated for a current state and then reduced. After generating the reduced set of &agr; values, the full set of &bgr; values may be generated for calculation of log-likelihood values. By calculating a set of &agr; values that may be decimated by, for example, N, the amount of memory required to store the &agr; values used in subsequent computations is reduced.

Generating log-likelihood values in a maximum a posteriori processor

An iterative decoding system for intersymbol interference (ISI) channels has a module for extracting bit reliabilities from a partial response (PR) channel, an iterative decoder, and a module for updating the bit reliabilities. A transmitter parses a data sequence into blocks that are encoded to generate a sequence of codewords. By encoding, a correlation among the bits of each codeword output to the PR channel is created. A maximum likelihood sequence detector (MLSD) in the receiver produces estimates of transmitted bits from samples of the output from the PR channel. The MLSD detector has a priori knowledge of typical error events that can occur during transmission through the channel. Along with the bit estimates, at each time instant the MLSD detector generates set of error event likelihoods. These error event likelihoods are then converted into bit reliabilities that, together with estimates for the transmitted bits, are used to recalculate the bit reliabilities using the knowledge of the relation between bits within a codeword. The iterative decoder uses this soft input information (bit reliabilities and bit estimates) for each iteration of decoding to improve i) the estimate of the bit reliabilities, ii) the decisions of what bit has been transmitted, and iii) calculations for the error event likelihoods for the next iteration. These error event likelihoods are then converted into bit reliabilities that, together with estimates for the transmitted bits, are used by the iterative decoder to recalculate the bit reliabilities using the knowledge of correlation among bits within the codeword. The error event likelihoods may be updated using the updated bit reliabilities, and the updated error event likelihoods are then converted to new bit reliabilities for the next iteration. In an iterative manner, increasing those bit reliabilities that tend to show increasing confidence for corresponding decoded bits (i.e., corresponding Viterbi decisions) between iterations, while decreasing those reliabilities that tend to show decreasing confidence for corresponding decoded bits, tends to drive the iterative decoding scheme to fewer iterations while maintaining a predetermined probability of error.

Interative decoding based on dominant error events

The present invention is a method and system for generating soft outputs by taking advantage of the fact that, in PR channels, there exists a set of error event sequences that occurs more often than others. This results in a soft output extractor with significantly lower complexity than the prior art SOVA system. Minimum distance error event sequences are identified and soft information or bit reliabilities are extracted from the PR channel based on the identified sequences. The soft information is then used by an iterative error control decoder correct errors more efficiently.

Method and apparatus for extracting reliability information from partial response channels

An iterative decoder employs detection and post-processing of channel output samples to generate soft output vales for encoded data provided to the decoder for one or more iterations of decoding. The channel output samples account for user data encoded with concatenated codes. For one or more other iterations, the reliability values of the soft values of the prior iteration are updated, generating soft output data for the decoder for the current iteration of decoding. A detector may use a soft-output Viterbi algorithm (SOVA) to detect encoded data from channel output samples, and the SOVA algorithm may be implemented by a Viterbi algorithm generating hard decisions from the output channel samples followed by post-processing to generate and update reliability values for the soft-output values based on the hard decisions and output channel samples. In one implementation, generating soft-output values from output channel samples by a detector employs the full soft-output Viterbi algorithm (SOVA) during the first iteration. For subsequent iterations, the post-processor of the full SOVA algorithm is used without a Viterbi detector.

Iterative decoding with post-processing of detected encoded data

A data transmission system employs an iterative decoder that applies decision feedback equalization (DFE) to channel output samples of a packet of data. The iterative decoder receives a stream of channel output samples as packets that may, for example, be read from a sector of a recording medium. Each packet may represent user data encoded, for example, with a concatenated code formed from N component codes, N a positive integer. The iterative decoder employs I decoding iterations, I a positive integer. DFE employs two filters: a feedforward filter and a feedback filter. The feedforward filter, which may be a whitened-matched filter used for detection, shifts dispersed channel output energy into the current sample. The feedback filter cancels energy of trailing inter-symbol interference from previous symbols. In each iteration by the iterative decoder, the DFE is applied to channel output samples of a packet by filtering with the feedforward filter, and then filtering with the feedback filter to cancel interference energy in the current sample from previous samples. The feedback filter uses soft information corresponding to tentative decisions for decoded data of the packet. During the first iteration, the soft information for applying DFE to the current sample is derived from the slicer output directly, and during the second and subsequent iterations the soft data of the decoder is provided to the feedback filter of the equalizer as soft information.

Iterative decoding of data packets employing decision feedback equalization

A data recording system employs parallel iterative decoding of soft output samples representing encoded data read from a storage medium. The iterative decoder reads packets of data from a sector of the medium, each packet containing soft output samples representing data encoded with a concatenated code formed from N component codes, N a positive integer. The iterative decoder employs I decoding iterations, I a positive integer. Each packet has a length substantially equal to the sector length divided by N. For an exemplary magnetic recording system, encoded data read from a sector of a magnetic medium is partitioned into N packets of length 4096/N. The first packet is passed to the first component code decoder of a parallel iterative decoder. When the second packet is ready to be passed to the first component code decoder, the decoded output values of the first packet in the first decoder are passed to the second component code decoder. The second packet is then applied to the first component code decoder. This operation is repeated until the last (Nth) packet is input to the first component code decoder. When the last (Nth) packet is input to the first component code decoder, all data in a sector is stored within the parallel iterative decoder and servo mode of the magnetic recording system is enabled. During servo mode, the iterative process of the iterative decoder begins. The decoded output of the first packet in the last (Nth) component code decoder is now input to the first component code decoder and the second iteration starts. Each packet in the corresponding component code decoder is then circularly shifted to the next component code decoder. After repeating the decode and shift operation for I iterations, each successive packet output from the last (Nth) component code decoder is provided as the decoded user data.

Iterative decoding of concatenated codes for recording systems

An iterative decoder arranges calculation of updated reliability values for a current iteration of an iterative decoder so as to reduce the number of comparison operations. The variables for the magnitude and sign of the updated reliability value are initialized. A search of the previous reliability values generates first and second minimum magnitude values for each row (if the iterative decoder is decoding in the horizontal direction) or column (if the iterative decoder is decoding in the vertical direction). A test determines whether the magnitude of the previous reliability value is greater than the first minimum magnitude value m . If so, the magnitude of the updated reliability value is set as the value m . Otherwise, the magnitude of the updated reliability value is set as the second minimum magnitude value m . The sign of the updated reliability value is tracked and assigned once the updated reliability value is set.

Generating reliability values for iterative decoding of block codes

A detector system employing a Viterbi algorithm includes an apparatus and method which constructs a double-state trellis structure for determining a most likely received symbol sequence with respect to an observed sequence of channel output samples. In the double state trellis, pairs of states are identified having equivalent branch metric values which also have a same decision during a path select, thus allowing these pairs of states to share a compare operation of a previous state metric. Consequently, to calculate an updated or current state metric value, an add, compare and select (ACS) circuit may compare only the previous state metric values to determine a minimum value for a transition between two states while combining each previous state metric value with its corresponding branch metric to provide an updated or current state metric value.

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Kearny, NJ, United States of America

Inkyu Lee