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
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"Several approaches are known to realize such an adaptive complexity behavior for decoding trellis based codes, as e.g. the T-Algorithm . However, the T-Algorithm is more beneficial for codes with a large number of trellis states, which is not the case in the ATSC system, where trellis coded modulation (TCM) with a four state encoder is used . "
[Show abstract][Hide abstract] ABSTRACT: To realize energy efficient broadcasting receivers the complexity of the applied algorithms should be adaptive, i.e. the number of required operations should decrease with improving reception conditions. In case of channel decoding algorithms this adaptive behavior can be achieved by syndrome based decoding methods, which allow a reduction of the receiver's energy consumption in above-average reception conditions. This paper extends the syndrome decoding approach to trellis coded modulation, as applied in the ATSC DTV system. Furthermore, the application of the syndrome concept to receivers based on Turbo equalization is considered. In this case the syndrome approach enables a reduction of the soft output decoder's complexity.
"Parallel decoding benefits from SD as well, because it allows to partition the received sequence without any overlapping overhead, which clearly improves the efficiency of the parallel implementation. While alternative approaches like the T-Algorithm  reduce the decoding complexity in high SNR regions as well, the avoidance of overlapping relies on a specific property of the syndrome decoder. Two aspects will be evaluated in this paper: The efficiency of the SMP based syndrome decoder implementation in terms of speedup and the possible reduction of clock frequency exploiting adaptive complexity. "
[Show abstract][Hide abstract] ABSTRACT: This paper investigates the implementation of a trellis based syndrome decoder on symmetric multiprocessor (SMP) platforms. Two advantages of the proposed approach will be exposed: First, compared to conventional parallel Viterbi decoder implementations, the syndrome decoder achieves a higher parallel efficiency in terms of speedup on the SMP platform. This is realized by reducing the computational overhead of the parallel Viterbi algorithm implementation. Second, it offers an adaptive complexity, i.e. the number of decoding operations decreases with improving transmission conditions. This property can be exploited to reduce the average energy consumption of a radio receiver. Measurement results are shown for two SMP platforms: ARM's MPCore and Intel's Core i7.
". • Select only a subset of the full number of trellis states, used in Joint Reduced State Sequence Estimation (JRSSE) ,  JMA  and T-algorithm . Compared to JDDFSE, JRSSE works with Set Partitioning, JDDFSE might be considered as a special case. "
[Show abstract][Hide abstract] ABSTRACT: We target M -ary data sequence estimation over time-variant frequency selective fading channels subject to co- channel interference (CCI). A novel joint reduced state sequence estimator is presented cancelling a dominant interferer by joint detection. It works similar to a Joint M-Algorithm (JMA), but with a metric minimizing the expectation of the Euclidian distance between received signal and convolution of channel and data hypotheses. In this way, we overcome the well known dependence of reduced search techniques on the partial channel energy. Co- Channel interference (CCI) as well as intersymbol-interference (ISI) is modelled as Gaussian noise and considered in the metric. This approach dramatically reduces the number of states to obtain a certain performance compared to JMA. The resulting joint breadth first tree detector combines good performance with low complexity. For low velocities, the technique can work with an initial channel estimate as data detector only, termed Joint Improved M-Algorithm (JIMA). For higher velocities, two Joint Iterative Channel Data Estimation (JICDE) techniques are discussed. The complexity of JIMA is linear in the alphabet size of each individual user and approximately linear in the channel memory. In comparison to known reduced state sequence estimators, no Front End Prefilter (FEP) to shorten the channel is needed. We propose a receiver with fixed parameterization. The GSM/EDGE signal model is used, 16 states are sufficient for detection of an 8-PSK signal, 3 states are needed for a GMSK-signal to yield performance similar to a 64 state Joint Delayed Decision Feedback Sequence Estimation (JDDFSE).