Joint spare channel estimation and decoding for orthogonal frequency division multiplexing using combined message passing

Yuan, Zhengdao; Zhang, Chuanzong; Wang, Zhongyong; Guo, Qinghua; Xi, Jiangtao

doi:10.1049/iet-com.2017.1218

Cited by 2 publications

(3 citation statements)

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“…To ease the closed-form derivation, we can apply a change of variable that would rather consider the precision λ n 1/σ 2 n and its associated conjugate prior distribution than working directly with p(σ 2 n ). Leveraging known results in Bayesian analysis involving Gaussian distributions [42][43][44], the conjugate prior for λ n under a Gaussian likelihood model is given by the Gamma distribution:…”

Section: Closed-form Llr Expression With Statistical Csi On the Noisementioning

confidence: 99%

“…Leveraging known results in Bayesian analysis involving Gaussian distributions [ 42 , 43 , 44 ], the conjugate prior for

under a Gaussian likelihood model is given by the Gamma distribution:

where

is the standard Gamma function with parameters

and

. Note that

and

denote the shape parameter and the scale parameter of the Gamma distribution.…”

Section: Closed-form Llr Expression With Uncertain Noise Variancementioning

confidence: 99%

“…Leveraging known results in the Bayesian analysis [ 42 , 43 , 44 ], the conjugate prior for

under a Laplacian likelihood model is given by the Gamma distribution:

…”

Section: Closed-form Llr Expression For Pulsed Jamming Scenariosmentioning

confidence: 99%

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Low Complexity Robust Data Demodulation for GNSS

Ortega¹,

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et al. 2021

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In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process. In this contribution, we first derive two closed-form expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty.

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Section: Closed-form Llr Expression With Statistical Csi On the Noisementioning

confidence: 99%

“…Leveraging known results in Bayesian analysis involving Gaussian distributions [ 42 , 43 , 44 ], the conjugate prior for

under a Gaussian likelihood model is given by the Gamma distribution:

where

is the standard Gamma function with parameters

and

. Note that

and

denote the shape parameter and the scale parameter of the Gamma distribution.…”

Section: Closed-form Llr Expression With Uncertain Noise Variancementioning