Generalized Moment-Based Method for SNR Estimation

Bakkali, Mohamed El; Stéphenne, Alex; Affes, Sofiène

doi:10.1109/wcnc.2007.416

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…The tail-biting, also known as cyclic encoding, ensures that the initial and termination states of each component encoder are the same [23,24]. In previous research, tailbiting convolutional codes could overcome the rate loss caused by initializing the coder with known bits [25], and the tail-biting encoding structure could solve the existing error-level phenomenon in turbo codes [26].…”

Section: Tail-biting Encoding Structurementioning

confidence: 99%

Improved encoding structure and decoding algorithms for spinal codes

Huang

Wang

2022

J. of Syst. Eng. Electron.

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To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding (A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination (AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

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Section: Tail-biting Encoding Structurementioning

confidence: 99%

Improved encoding structure and decoding algorithms for spinal codes

Huang

Wang

2022

J. of Syst. Eng. Electron.

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“…In [26], the expressions for the second-order and fourth-order moments at antenna-element i are: (12) where Ω and N 0 are, respectively, the signal and the noise powers; and k i;a and k i;ω are, respectively, the Rician and noise kurtosis. In our article, we consider an additive white Gaussian noise (AWGN), i.e., k i;ω = 2, [26].…”

Section: New Estimator With Angular Distribution Selectionmentioning

confidence: 99%

“…In our article, we consider an additive white Gaussian noise (AWGN), i.e., k i;ω = 2, [26]. As in [14], we consider an estimated SNR to reduce the noise bias.…”

Section: New Estimator With Angular Distribution Selectionmentioning

confidence: 99%

A new low-complexity angular spread estimator in the presence of line-of-sight with angular distribution selection

Bousnina

Stéphenne

Affes

et al. 2011

EURASIP J. Adv. Signal Process.

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This article treats the problem of angular spread (AS) estimation at a base station of a macro-cellular system when a line-of-sight (LOS) is potentially present. The new low-complexity AS estimator first estimates the LOS component with a moment-based K-factor estimator. Then, it uses a look-up table (LUT) approach to estimate the mean angle of arrival (AoA) and AS. Provided that the antenna geometry allows it, the new algorithm can also benefit from a new procedure that selects the angular distribution of the received signal from a set of possible candidates. For this purpose, a nonlinear antenna configuration is required. When the angular distribution is known, any antenna structure could be used a priori; hence, we opt in this case for the simple uniform linear array (ULA). We also compare the new estimator with other low-complexity estimators, first with Spread Root-MUSIC, after we extend its applicability to nonlinear antenna array structures, then, with a recently proposed two-stage algorithm. The new AS estimator is shown, via simulations, to exhibit lower estimation error for the mean AoA and AS estimation.

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“…This method classifies the modulation order for MPSK under a known SNR condition. However, the SNR estimation methods are only applicable for a known modulation [5], [6]. In [7], a classification method based on moments was presented without knowledge of SNR.…”

Section: Introductionmentioning

confidence: 99%

PSK and QAM Classiﬁcation by Likelihood under Unknown SNR Condition

Nishijima

Oka

Ata

2016

Journal of Signal Processing

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Modulation classification is a key technology in current wireless communication systems. The technology is used to find the modulation employed or estimate channel state information (CSI). In this paper, a modulation classification method is proposed for phase shift keying (PSK) and quadrature amplitude modulation (QAM) under an unknown signal-to-noise ratio (SNR) condition. The method works without carrier synchronization and is based on likelihood estimation using the probability density function (pdf) of the received signal envelope. The superior classification error rate of the proposed method is demonstrated by computer simulations.

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Generalized Moment-Based Method for SNR Estimation

Cited by 16 publications

References 5 publications

Improved encoding structure and decoding algorithms for spinal codes

Improved encoding structure and decoding algorithms for spinal codes

A new low-complexity angular spread estimator in the presence of line-of-sight with angular distribution selection

PSK and QAM Classiﬁcation by Likelihood under Unknown SNR Condition

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