Cumulative Interference

Pirmoradian, Mahdi; Politis, Christos

doi:10.1109/mvt.2010.937848

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Therefore F out can be expressed by (26), shown at the bottom of the page, which is the accurate expression of outage probability. It can be simplified by using Gauss-Hermite quadrature given in (27), shown at the bottom of the page, where Y is defined in (28), s is defined in (18) and W is…”

Section: A Probability Of Outagementioning

confidence: 99%

“…Interference analysis for wireless communications becomes more difficult by the complexity of the propagation environment. The probability model of the interference has been evaluated in the literature for only pathloss [27], pathloss and shadowing [6], pathloss plus shadowing and Rayleigh fading [2], [11], [13] or Rician Fading [2], [14], [29] and Nakagami-m fading [14]- [17]. In this paper we propose an analytical model that integrates pathloss, log-normal shadowing and Nakagami-m fading interferers.…”

Section: Introductionmentioning

confidence: 99%

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Co-Channel Interference Modeling in Cognitive Wireless Networks

Zhao

et al. 2014

IEEE Trans. Commun.

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Cognitive radio is a promising technology for sharing the underutilized frequency bands that have been licensed to primary users. However, due to the uncertainty in detecting the existence of the primary user, the secondary user may interfere with the primary users when both primary and secondary users are active simultaneously. Therefore, understanding the interference and its consequences on the cognitive network is critical. Unlike the statistical models previously reported in the literature that aim at approximation of the interference, based on the solid mathematical analysis, we propose an accurate model for describing the co-channel interference with probability density function, cumulative distribution function, mean, and variance of the interference suffered by the primary users. The proposed model not only takes into account a number of factors, such as the spectrum-sensing scheme, the spatial distribution of secondary users, and the channel conditions, including shadowing and Nakagami fading, but also gives an exact mathematical expression of the influences from these factors. The developed framework supports practical applications such as evaluating the cognitive network of any spatial shape and density of the secondary users and the methods of power control and spectrum sensing used by the secondary users. Simulation results are provided to verify the effectiveness of the analytical model.

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Section: A Probability Of Outagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Co-Channel Interference Modeling in Cognitive Wireless Networks

Zhao

et al. 2014

IEEE Trans. Commun.

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“…However, such intelligent networks are imposing many challenges including the limited bandwidth allocated generally by the industrial, scientific and medical band radio managers. Bandwidth limitation is caused by the ever‐increasing usage demand for both licensed and unlicensed radio frequency spectrum by various wireless systems such as WiFi and Bluetooth [4]. In addition, despite the growing requirement of frequency bands for communication, the licensed spectrum usage is still underutilised [5, 6].…”

Section: Introductionmentioning

confidence: 99%

Link‐delay and spectrum‐availability aware routing in cognitive sensor networks

Tran-Dang

Kim

2020

IET Communications

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“…The ubiquity of the networks increases the necessary demand for more available spectrum resources and results in that the spectrum is becoming overcrowded [5]. However, according to the studies sponsored by the Federal Communications Commission(FCC), a large portion of the assigned spectrum is used sparsely and inefficiently in the utilization of frequency ranges up to 85% with a high variance in time [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, according to the studies sponsored by the Federal Communications Commission(FCC), a large portion of the assigned spectrum is used sparsely and inefficiently in the utilization of frequency ranges up to 85% with a high variance in time [5], [6]. Effectively using of limited channel resources is a method to avoid interferences.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model Design and Transmission Rate Optimize with Interference Temperature Constraints in Cognitive Radio Sensor Networks

Zheng

Qin

Gao

et al. 2011

2011 7th International Conference on Wireless Communications, Networking and Mobile Computing

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Properly modeling the aggregated interferences from the entire cognitive radio sensor networks is necessary to effectively protect these incumbent users. In this paper, we present a prominent interference avoiding mechanism based on a mathematical hybrid modeling for the cumulative interference to the primary network as well as the probe link from secondary networks. For the purpose of the paper, we consider the interference temperature constraints and secondary network's Qualityof-Service requirements, as well as obtain the optimal transmitter power for maximizing the system transmission rate.

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Cumulative Interference

Cited by 7 publications

References 9 publications

Co-Channel Interference Modeling in Cognitive Wireless Networks

Co-Channel Interference Modeling in Cognitive Wireless Networks

Link‐delay and spectrum‐availability aware routing in cognitive sensor networks

Hybrid Model Design and Transmission Rate Optimize with Interference Temperature Constraints in Cognitive Radio Sensor Networks

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