Space-Time Block Codes

Rajan, B. Sundar

doi:10.1016/b978-0-12-396499-1.00010-8

Cited by 1 publication

(2 citation statements)

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“…As compared to SISO systems, a Gibbs sampling implementation such as in [20] may have an impractically slow convergence due to the high-dimensional and multimodal distributions in MIMO systems. The strategy of annealing [31]- [33] combined with multiple random restarts [34]- [37] is hence proposed here to improve the convergence speed. 2) An alternative Bayesian solution for modulation classification in MIMO-OFDM systems that leverages mean field variational inference [38] is proposed, based on the same latent Dirichlet prior selection.…”

Section: ) a Modulation Classification Technique Is Proposed Basedmentioning

confidence: 99%

“…Remark 2: When applying Gibbs sampling to practical problems, in particular those with high-dimensional and multimodal posterior distribution p(Θ j |Θ u Θ j , Θ e ), slow convergence may be encountered due to the local nature of the updates. One approach to address this issue is to run Gibbs sampling with multiple random restarts that are initialized with different feasible solutions [34]- [37]. Moreover, within each run, simulated annealing may be used to avoid low-probability "traps."…”

Section: Gibbs Samplingmentioning

confidence: 99%

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Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

Liu

Simeone

Haimovich

et al. 2016

IEEE Trans. Veh. Technol.

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The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The Gibbs sampling method converges to the optimal Bayesian solution and, using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assume that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis and on prior Bayesian methods based on the 'superconstellation' method.

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