Method and processing system for estimating likelihood ratios for input symbol values
AssigneeThe Aerospace Corporation
InventorsGee L. Lui, Kuang Tsai
Life of this patent
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Abstract
A method and processing system for determining a soft decision metric for each input symbol, including and/or performing the steps of: computing trellis branch metrics based on a received sample sequence; updating initial state metrics from time (n−1) with a Viterbi algorithm (VA); constraining M trellises differently at time n such that only state transitions caused by an input value associated with a particular trellis are allowed; executing the VA on the M constrained trellises for a finite number (Nd) steps; and computing likelihood ratios by taking a difference of a maximum state metric at time (n+Nd) for each trellis with a maximum state metric of a reference trellis.
Claims16
A method for approximating a posteriori probabilities of inputs to a finite state machine, comprising the steps of:
- exercising a Viterbi algorithm over M differently constrained trellises, one trellis for each of M possible input symbol values;
- and
- taking a difference of maximum state metrics of the differently constrained trellises to form likelihood ratios.
A method for determining a soft decision metric for each input symbol, comprising the steps of:
- computing trellis branch metrics based on a received sample sequence;
- updating initial state metrics from time (n−1) with a Viterbi algorithm (VA);
- constraining M trellises differently at time n such that only state transitions caused by an input value associated with a particular trellis are allowed;
- executing the VA on the M constrained trellises for a finite number (Nd) steps;
- and
- computing likelihood ratios by taking a difference of a maximum state metric at time (n+Nd) for each trellis with a maximum state metric of a reference trellis.
A method for estimating likelihood ratios of the input symbol values of an encoded sequence, comprising the steps of:
- providing an optimal decision metric for symbol-by-symbol detection;
- making relaxations or simplifications to the optimal decision metric to provide an approximation to the optimal decision metric;
- and
- processing a sample sequence employing a Viterbi algorithm (VA) and differently constrained trellises to implement the approximation to the optimal decision metric.
A processing system for estimating likelihood ratios for input symbol values, comprising:
- a processor configured to compute likelihood metrics on a symbol-by-symbol basis to determine an approximation to a maximum a posteriori estimate of a given symbol based on computing a maximum correlation of a received signal with all possible transmitted signals using a Viterbi algorithm over differently constrained trellisses.
A processing system for estimating likelihood ratios for input symbol values, comprising:
- a decoder configured to implement a Viterbi algorithm (VA) on M differently constrained trellises for M values that an input to the decoder can take on at any given instant.
Dependencies are inferred from the claim text.