Motorola Corporation

Meir Ariel

Reuven Meidan

Ofer Amrani

A syndrome assisted iterative decoder ( ) uses iterative processing to decode an input sequence. A syndrome-assisted decoder ( ), which may operate independently of a soft-output decoder, provides a stopping algorithm for the soft output decoder ( ). When the syndromes s and s generated for sequence v and v , respectively, both equal 0, the soft output decoder is finished decoding the sequence of data. Additionally, the syndrome-assisted decoder can generate a modified error trellis used by the soft output decoder whereby the soft-output decoder operates on a simplified trellis providing improved decoder performance.

Syndrome assisted iterative decoder for turbo codes

Soft output decoding of signals represented by a trellis of block length N divided into windows includes a step of decoding a forward recursion to a location that is I sections from the beginning of the window. An optimum state is detected at the end of the forward recursion. A backward recursion is performed from the determined state at the end of the window back to the beginning of the window to define a set of known backward recursion state metrics. Soft outputs are calculated during the backward recursion using the stored forward recursion state metrics, the backward recursion state metrics, and branch metrics at each state.

Soft output decoder for convolutional codes

A soft-decision decoder computes a maximum likelihood estimated transmitted code from a demodulated received signal with less complexity compared to a conventional Viterbi soft-decision decoder. A symbol-by-symbol detector determines a hard-decision vector from a demodulated received signal vector. A syndrome calculator multiplies the hard-decision vector by a scalar parity check matrix to calculate a syndrome vector. A syndrome modifier examines the syndrome vector for predetermined syndrome patterns corresponding to isolated errors in the hard-decision vector. Syndrome patterns are removed from the syndrome vector to create a modified syndrome vector, and the corresponding error in the hard-decision vector is recorded in an estimated error vector. A syndrome-based decoder uses the modified syndrome vector to create a simplified error trellis to produce a remaining error vector. Subtracting the estimated error vector and the remaining error vector from the hard-decision vector creates a maximum likelihood estimated transmitted code vector.

Soft-decision syndrome-based decoder for convolutional codes

A syndrome-based channel quality or message structure determiner (160) uses syndrome vectors to estimate the quality of a received signal in a variable message structure communication system. A digital signal from analog-to-digital converter (150) is separated into multiple branching paths, one for each potential message structure type. In a single path, a digital demodulator (221) demodulates the digital signal to produce a demodulated received signal. A deinterleaver (222) deinterleaves the demodulated received signal, and a symbol-by-symbol detector (2231) hard-decision detects the deinterleaved signal. For each hard-decision vector, at least one syndrome calculator (2241) calculates a syndrome vector. The syndrome vector is analyzed by a syndrome error estimator (2251) having an associated syndrome pattern memory (2271). A comparator (260) analyzes error counter totals from each syndrome error estimator.

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Meir Ariel