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

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.