In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabili...
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In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm, forward-backward, and sum-product algorithms, shows that a basic metric emerges naturally under very general causality and finite-memory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-memory condition. This conditional Markov property, although seldom met exactly in practice, is shown to represent a reasonable and useful approximation in all considered cases. We consider, as examples, linear predictive and noncoherent detection schemes. While good performance for increasing complexity can often be achieved with a finite-memory detection strategy, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity.
In this paper, by applying the concept of linear prediction, which is widely used for fading channels, to phase-uncertain communications, we generalize existing linear predictive detection algorithms for transmission ...
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In this paper, by applying the concept of linear prediction, which is widely used for fading channels, to phase-uncertain communications, we generalize existing linear predictive detection algorithms for transmission over channels with phase noise and frequency offset. This approach leads to the derivation of detection algorithms, which are referred to as phasor linear predictive (pLP), for trellis-based maximum a posteriori (MAP) sequence detection (based on the Viterbi algorithm) and MAP symbol detection: trellis-based (using the forward-backward algorithm) and graph-based (using the sum-product algorithm). The effectiveness of the, proposed pLP detection algorithms is evaluated for several communication schemes. The derived algorithms outperform previously appeared finite-memory detection solutions in terms of robustness against fast channel dynamics. Moreover, the proposed detection strategy lends itself to attractive extensions to adaptive schemes.
In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of soft-input soft-ouput maximum a p...
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In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of soft-input soft-ouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encompassing unknown parameters, either stochastic or deterministic. The proposed solutions represent effective ways to reach this goal. The described algorithms are grouped into three categories: i) we first introduce algorithms for adaptive iterative detection, where the unknown channel parameters are explicitly estimated;ii) then, we consider finite-memory iterative detection algorithms, based on ad hoc truncation of the channel memory and often interpretable as based on an implicit estimation of the channel parameters;and iii) finally, we present a general detection-theoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.
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