Analysis of Wireless Communication Systems in the Presence of Non-Gaussian Impulsive Noise and Gaussian Noise

Niranjayan, S.; Beaulieu, N.C.

doi:10.1109/wcnc.2010.5506444

Cited by 13 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, the background noise can be modeled as a complex Gaussian distribution, implying that the envelope follows a Rayleigh distribution. 25 In terms of the latter one, the impulsive noise generated from operating welders covers a wide range of frequency bands, ranging from 30 MHz to 3 GHz, like, the results shown in Figure 4. Moreover, such kind of noise exhibits a bursty characterization both in frequency and time domains.…”

Section: Classification Methodsmentioning

confidence: 83%

“…As shown in Figure 4, the collected industrial noise is a mixture of background noise and impulsive noise. Usually, the background noise can be modeled as a complex Gaussian distribution, implying that the envelope follows a Rayleigh distribution 25 . In terms of the latter one, the impulsive noise generated from operating welders covers a wide range of frequency bands, ranging from 30 MHz to 3 GHz, like, the results shown in Figure 4.…”

Section: Measurement Methodologymentioning

confidence: 96%

See 1 more Smart Citation

Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

et al. 2020

View full text Add to dashboard Cite

In this paper, we performed a series of measurement campaigns on the wireless electromagnetic noise for two typical industrial welding scenarios. On this basis, we investigate the characterization of the impulsive noise mainly from two aspects, that is, frequency and time domains. To start with, a novel denoising method based on the dynamic threshold is proposed to identify the desirable impulse noise from the background noise. Then, in the frequency domain, we focus on the power distribution of impulsive noise at different frequency bands. Results exhibit a shadow effect with regard to different frequency bands and we characterize it by using a linear function with a Gaussian distribution. Besides, analyses on the power spectrum correlation for different polarization modes and scenarios are also provided. In the time domain, we performed a series of statistical analyses from aspects of pulse amplitude, duration, and elapse interval to characterize the impulsive noise. Furthermore, three empirical distributions are employed to depict the parameters' variation tendency, that is, Cauchy distribution for amplitude, Gamma distribution for pulse duration, and exponential distribution for pulse interval. Finally, a firstorder two-states Markov method is proposed to model the industrial noise. Simulation results are proved to be

show abstract

Section: Classification Methodsmentioning

confidence: 83%

Section: Measurement Methodologymentioning

confidence: 96%

Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In some cases, however, communication systems observe Gaussian noise as well as non-Gaussian noise with impulsive characteristics [8]- [12]. Some examples of such an impulsive noise environment are multiple access interference of mobile communication systems with short and burst packets (low duty cycle), radar clutter, and acoustic noise in underwater signal detection [13]. In power line communication (PLC) systems, electromagnetic (EM) noise is caused by switching transients in the power network, where the noise has a short duration with random occurrence and a high-power spectral density [14].…”

mentioning

confidence: 99%

Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

Nam

2021

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

This article proposes an opportunistic scheduling scheme based on adaptive modulation for users in a mixed noise environment, where some users are under additive white Gaussian noise (AWGN) and other users are exposed to impulsive noise. Unlike the scenario, where all the users are in an AWGN environment, the maximum signal-to-noise ratio (SNR) scheduler does not provide the maximum capacity if users are in a mixed noise environment. In the proposed scheduling scheme, called maximum rate scheduler, the user with the highest rate (or highest modulation order), instead of highest SNR, is selected. To evaluate the performance of the proposed scheduling scheme, the analyses of average spectral efficiency, outage probability and system capacity are provided along with a simple calculation of SNR thresholds for adaptive modulation in an impulsive noise environment satisfying target symbol error rate (SER). Simulation results illustrate that system capacity with the proposed scheduling given target SER = 10 −2 and the average spectral efficiency are improved by 156% and 124%, respectively, at SNR = 5 dB when a strong impulsive noise is present.

show abstract

“…This will present an appropriate background before our work is introduced in Chapters 4-6. Detection and localization of weak signals in non-Gaussian noise are problems of interest in several applications such as sonar [12,[19][20][21][22], radar [23][24][25][26][27][28][29], medical equipment [30] and wireless communications [31][32][33][34][35][36][37][38][39]. In this work, we are interested in shallow water applications in which ambient noise is known to be leptokurtic (heavy-tailed) in many situations [8,9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Noise has been often assumed to have Gaussian probability distribution in conventional studies. Although this assumption makes the analysis of the problem simpler and more tractable, it is not valid in cases such as multiple access interference in communication [31,35], sea clutter in radars [23,25,29], and ambient noise in shallow water [8,9,40,41].…”

Section: Source Detection In Non-gaussian Noisementioning

confidence: 99%

Nonlinear techniques for source detection and localization in shallow ocean with non-Gaussian noise

Ardekani¹

View full text Add to dashboard Cite

This research would not have been possible without the support of several people and institutes who have supported and encouraged me to the completion of this dissertation. I could probably have written a full chapter to acknowledge those, who, in different ways, have supported me during my PhD life at Nanyang Technological University (NTU). First and foremost, my utmost gratitude to my supervisor, Assoc. Prof. Benjamin Premkumar who was abundantly helpful and offered me invaluable support and guidance during the lifetime of this research. I also wish to express my deepest gratitude toward Prof. G.V. Anand from the Indian Institute of Science without him this study would not have been successful. He has greatly contributed to this project and constantly advised me during the completion of my research. It was a great blessing for me to have the opportunity to work with him. I would also like to thank Assoc. Prof. C.T. Lau, my co-supervisor, who has provided me support and feedback whenever it was required. I would like to appreciate the Defense Science and Technology Agency (DSTA) of Singapore, the sponsor of this project for their support and their feedbacks throughout the project. I would also like to convey thanks to Agency for Science, Technology and Research of Singapore (A*Star) for granting me the scholarship to pursue my PhD at NTU and to strengthen my passion in research. I would also like to thank the staff and technicians in Centre for Multimedia and Network Technology (CeMNet) for the assistance and the facilities they provided me. My special thanks also to all my friends at CeMNet especially Hari, Ashish and Xionghu for creating a friendly environment and making my everyday research life a pleasant one. I have benefited a lot from different interesting discussions that we have had. Last but not the least, I wish to express my love and gratitude to my beloved family, for their endless love and support, throughout my studies and God, for giving me the strength, courage and hope to follow my dreams, no matter how hard or impossible they seem. i

show abstract

Analysis of Wireless Communication Systems in the Presence of Non-Gaussian Impulsive Noise and Gaussian Noise

Cited by 13 publications

References 11 publications

Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

Measurements and statistical analyses of electromagnetic noise for industrial wireless communications

Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

Nonlinear techniques for source detection and localization in shallow ocean with non-Gaussian noise

Contact Info

Product

Resources

About