A Cumulant-Based Characterization of the Aggregate Interference Power in Wireless Networks (original) (raw)
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Ieee Transactions on Vehicular Technology, 2010
The distribution of the aggregate interference power in large wireless networks has gained increasing attention with the emergence of different types of wireless networks such as ad hoc networks, sensor networks, and cognitive radio networks. The interference in such networks is often characterized using the Poisson point process (PPP). As the number of interfering nodes increases, there might be a tendency to approximate the distribution of the aggregate interference power by a Gaussian random variable, given that the individual interference signals are independent. However, some observations in the literature suggest that this Gaussian approximation is not valid, except under some specific scenarios. In this paper, we cast these observations in a single mathematical framework and express the conditions for which the Gaussian approximation will be valid for the aggregate interference power generated by a Poisson field of interferers. Furthermore, we discuss the effect of different system and channel parameters on the convergence of the distribution of the aggregate interference to a Gaussian distribution.
On distribution of aggregate interference in cognitive radio networks
2010 25th Biennial Symposium on Communications, 2010
This paper analyzes the distribution of aggregate interference in cognitive radio networks. Poisson point spatial distribution model and average propagation path loss model are considered. All possible scenarios are classified into three typical cases, based on typical outage events. When the average number of nodes in the forbidden region is much smaller than one, the aggregate interference can be well approximated by the nearest one (nearest node dominates outage events). When the average number of nodes in the forbidden range is greater than one, the aggregate interference can be approximated by a Gaussian random variable (many nodes contribute to outage). When the average number of nodes in the forbidden range is slightly smaller than one, neither the nearest node approximation nor Gaussian one is accurate (a few near-by nodes are dominant), and higher order cumulants approximations or others are required. We derive the nearest interference distribution and give a simpler way to calculate the cumulants of the aggregate interference. † Y. Wen, S. Loyka and A. Yongacoglu are with the
Communications (QBSC), 2010
The distribution of the aggregate interference power in large wireless networks has gained increasing attention with the emergence of different types of wireless networks such as ad-hoc networks, sensor networks, and cognitive radio networks. The interference in such networks is often characterized using a Poisson Point Process (PPP). As the number of interfering nodes increases, there might be a tendency to approximate the distribution of the aggregate interference power by a Gaussian random variable given that the individual interference signals are independent. However, some observations in literature suggest that this Gaussian approximation is not valid except under some specific scenarios. In this paper, we cast these observations in a single mathematical framework and express the conditions for which the Gaussian approximation will be valid for the aggregate interference power generated by a Poisson field of interferers. Furthermore, we discuss the effect of different system and channel parameters on the convergence of the distribution of the aggregate interference power to a Gaussian distribution.
Analysis of the simulated aggregate interference in random ad-hoc networks
2014 IEEE 15th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2014
In this paper, we propose a new method for bias correction in the simulation of random wireless ad-hoc networks (WANETs), when the distribution of the node locations is modeled as a Poisson-Point-Process (PPP). The aggregate interference is the main limiting factor in WANETs, and dominates the achievable rate and thus also the network capacity. In the proposed method, a bias correction constant is added to the aggregate interference that is measured in each simulation iteration. The value of the constant is derived through stochastic geometry analysis. We prove that the proposed method can reduce the computational complexity by several orders of magnitude, while producing more accurate simulation results. This improved accuracy is also demonstrated by simulations. As an example, we prove that a bias corrected simulation with only 100 transmitters is sufficient to estimate the aggregate interference with an accuracy of 1%.
Доклады Белорусского государственного университета информатики и радиоэлектроники, 2009
The model is based on the traditional propagation channel model, which includes the average path loss as well as the large-scale and small-scale fading. In addition to these two traditional types of fading, a new concept of network-scale fading is introduced, which is due to a random spatial distribution of transmitters and receivers of the network over a large region of space occupied by the whole network. This new type of fading complements the small-scale (e.g. Rayleigh) and large-scale (e.g. lognormal) ones, is on the scale exceeding that of the other two and is independent of them. Its probability density function is derived for typical network configurations and propagation channel conditions. Network-level analysis of interference effects is given, which includes estimation of the average number of interferers, of the dynamic range of the interferers potentially capable of generating linear and non-linear distortion effects in the victim receiver, and of the outage probability. In many cases, the combined interference power at the receiver is shown to be dominated by the contribution of the strongest interferer. This analysis culminates in formulation of a tradeoff relationship between the network density and the outage probability. The positive role of linear filtering (e.g. in the antenna or in frequency filters of the receiver) in reducing the number and dynamic range of interfering signals, and/or in reducing the outage probability is quantified via a new statistical selectivity parameter (Q-parameter). The linear filtering allows increasing the network density by a factor of Q at the same outage probability.
Analysis of Interference in Wireless Networks
ArXiv, 2018
As wireless systems grow rapidly worldwide, one of the most important things, wireless systems designers and service providers faces is interference. Interference decreases coverage, capacity [1], and limits the effectiveness of both new and existing systems. It is very difficult to avoid because wireless communications systems must exist together in extremely complex signal environments. These environments are consisting of multiple operating wireless networks [2]. At the same instant, new technologies and signal sources in Wireless Local Area Networks (WLANs) and digital video broadcasting are jeopardized to wireless communications service. This article provides a survey and analysis of interference in Wireless Network and provides a taxonomy.
Accumulative Interference Modeling for Distributed Cognitive Radio Networks
Journal of Communications, 2009
A Cognitive Radio (CR) network should be able to sense its environment to adapt its communication so that it can utilize unused licensed spectrum without interfering with incumbent users. Properly modeling the expected interference from the entire CR network is there- fore very important to effectively protect these incumbent users. We model the accumulative interference generated from a large-scale CR
Interference Modeling of Cognitive Radio Networks
VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008
Cognitive radio (secondary) networks have been proposed as means to improve the spectrum utilization. A secondary network can reuse the spectrum of a primary network under the condition that the primary services are not harmfully interrupted. In this paper, we study the distribution of the interference power at a primary receiver when the interfering secondary terminals are distributed in a Poisson field. We assume that a secondary terminal is able to cease its transmission if it is within a distance of R to the primary receiver. We derive a general formula for the characteristic function of the random interference generated by such a secondary network. With this general formula we investigate the impacts of R, shadowing, and small scale fading on the probability density function (PDF) of the interference power. We find that when there is no interference region (R = 0), the interference PDFs follow heavy-tailed α-stable distributions. In case that a proper interference region is defined by a positive value of R, the tails of the interference power PDFs can be significantly shortened. Moreover, the impacts of shadowing and small scale fading on the interference PDFs are studied and the small scale fading is found to be beneficial in terms of reducing the mean value and outage probability of the interference power.
Aggregate Interference Modeling in Cognitive Radio Networks with Power and Contention Control
IEEE Transactions on Communications, 2000
In this paper, we present an interference model for cognitive radio (CR) networks employing power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference distributions under the first two control schemes are fitted by log-normal distributions with greatly reduced complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given via numerical studies as well.