Reduced-complexity turbo receivers for single and multi-antenna systems via variational inference in factor graphs

Mauri Nissilä, Subbarayan Pasupathy

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)


Many algorithms in signal processing and digital communications must deal with the problem of computing the probabilities of the hidden state variables given the observations, i.e., the inference problem, as well as with the problem of estimating the unknown model parameters. In this paper, we present an unified framework for approximate joint inference and estimation in the cases where an exact inference becomes computationally intractable. Specifically, approximate inference via variational minimization technique is obtained by operating a general message-passing algorithm in the distributed factor graph where the coupling between the multiple Markov chains is removed by minimizing the Kullback-Leibler distance between the original and the variational objective functions. Importantly, we demonstrate how this framework can be exploited in deriving reduced-complexity turbo receiver structures for coded single transmit antenna and space-time coded multiple transmit antenna systems over the multipath fading channels. Despite the significant reduction in complexity, the performance simulations showed that the derived turbo receivers are able to provide close to optimal performance.
Original languageEnglish
Title of host publication2004 IEEE International Conference on Communications
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Print)978-0-7803-8533-7
Publication statusPublished - 2004
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Communications, ICC 2004 - Paris, France
Duration: 20 Jun 200424 Jun 2004


ConferenceIEEE International Conference on Communications, ICC 2004


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