Breadth-first trellis decoding with adaptive effort

A breadth-first trellis decoding algorithm is introduced for application to sequence estimation in digital data transmission. The high degree of inherent parallelism makes a parallel-processing implementation attractive. The algorithm is shown to exhibit an error-rate versus average-computational-complexity behavior that is much superior to the Viterbi algorithm and also improves on the M-algorithm. The decoding algorithm maintains a variable number of paths as its computation adapts to the channel noise actually encountered. Buffering of received samples is required to support this. Bounds that are evaluated by trellis search are produced for the error event rate and average number of survivors. Performance is evaluated with conventional binary convolutional codes over both binary-synchronous-communication (BSC) and additive-white-Gaussian-noise (AWGN) channels. Performance is also found for multilevel AM and phase-shift-keying (PSK) codes and simple intersymbol interference responses over an AWGN channel. At lower signal-to-noise ratio Monte Carlo simulations are used to improve on the bounds and to investigate decoder dynamics