An empirical study of HF noise near Adelaide Australia

Giesbrecht, James E.

doi:10.1049/cp.2009.0086

Cited by 7 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research focuses on AMC in HF band, where the AWGN assumption no longer remains valid for all transmission times [17,18]. Instead, the noise varies between AWGN and bi-kappa distributions.…”

Section: Noise Modelmentioning

confidence: 99%

“…These features are generally extracted under the assumption that the modulated signals are corrupted by additive white Gaussian noise (AWGN). Although this assumption is valid in many communication environments, recent studies show that HF noise changes between AWG and bi-kappa distributions [17,18]. The effect of these two noise distributions has been taken into account during the design of the AMC algorithms proposed in [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robustness of digitally modulated signal features against variation in HF noise model

Hazza

Shoaib

Alshebeili

et al. 2011

J Wireless Com Network

View full text Add to dashboard Cite

High frequency (HF) band has both military and civilian uses. It can be used either as a primary or backup communication link. Automatic modulation classification (AMC) is of an utmost importance in this band for the purpose of communications monitoring; e.g., signal intelligence and spectrum management. A widely used method for AMC is based on pattern recognition (PR). Such a method has two main steps: feature extraction and classification. The first step is generally performed in the presence of channel noise. Recent studies show that HF noise could be modeled by Gaussian or bi-kappa distributions, depending on day-time. Therefore, it is anticipated that change in noise model will have impact on features extraction stage. In this article, we investigate the robustness of well known digitally modulated signal features against variation in HF noise. Specifically, we consider temporal time domain (TTD) features, higher order cumulants (HOC), and wavelet based features. In addition, we propose new features extracted from the constellation diagram and evaluate their robustness against the change in noise model. This study is targeting 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, and 64QAM modulations, as they are commonly used in HF communications.

show abstract

Section: Noise Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robustness of digitally modulated signal features against variation in HF noise model

Hazza

Shoaib

Alshebeili

et al. 2011

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…An automatic modulation classifier (AMC) is an essential part of a multi-standard communication system as it allows blind detection of the modulation schemes present in the received signal. However, designing an AMC for HF systems is not a trivial task, as the transmit signal is severely distorted due to ionospheric effects, multipath propagation, and non-Gaussian timevarying noise [2][3][4]. The non-Gaussian and time-varying nature of HF noise is a recent observation [3].…”

Section: Introductionmentioning

confidence: 99%

“…The non-Gaussian and time-varying nature of HF noise is a recent observation [3]. It has been noted in [4] that the HF noise follows Gaussian (G) or Bi-kappa (BK) distributions depending on day time. A BK distribution function is defined mathematically as [3] …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic modulation classification of digital modulations in presence of HF noise

Hazza

Shoaib

Alshebeili

et al. 2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Designing an automatic modulation classifier (AMC) for high frequency (HF) band is a research challenge. This is due to the recent observation that noise distribution in HF band is changing over time. Existing AMCs are often designed for one type of noise distribution, e.g., additive white Gaussian noise. This means their performance is severely compromised in the presence of HF noise. Therefore, an AMC capable of mitigating the time-varying nature of HF noise is required. This article presents a robust AMC method for the classification of FSK, PSK, OQPSK, QAM, and amplitude-phase shift keying modulations in presence of HF noise using feature-based methods. Here, extracted features are insensitive to symbol synchronization and carrier frequency and phase offsets. The proposed AMC method is simple to implement as it uses decision-tree approach with pre-computed thresholds for signal classification. In addition, it is capable to classify type and order of modulation in both Gaussian and non-Gaussian environments.

show abstract

Experimental analysis of noise level and channels availability for high frequency OFDM data transmission in NVIS propagation conditions

Bechet

Miclăuș

Miclaus

et al. 2016

2016 International Symposium on Electromagnetic Compatibility - EMC EUROPE

View full text Add to dashboard Cite

An empirical study of HF noise near Adelaide Australia

Cited by 7 publications

References 0 publications

Robustness of digitally modulated signal features against variation in HF noise model

Robustness of digitally modulated signal features against variation in HF noise model

Automatic modulation classification of digital modulations in presence of HF noise

Experimental analysis of noise level and channels availability for high frequency OFDM data transmission in NVIS propagation conditions

Contact Info

Product

Resources

About