Cellular network as a multiplicatively weighted voronoi diagram

Portela, J.N.; Alencar, Marcelo S.

doi:10.1109/ccnc.2006.1593171

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…The weights were specified by the effective cell association bias β between the macrocell and small cells which can be controlled to expand or shrink the small-cells' range. Precisely, β is a weighted ratio of the received SNR from macrocell BS versus small-cell BS [24]. For simplifying our analysis, we place all small-cell users on their cell edges, as illustrated in Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks

Ramamonjison

Bhargava

2015

IEEE Trans. Wireless Commun.

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To support the surge in wireless data traffic, the spectrum and energy efficiencies of cellular networks should be largely increased. Heterogeneous two-tier architecture has been identified as one key solution. However, small-cell deployment raises questions about the resulting energy efficiency and interference mitigation. Therefore, we propose an energy-efficient and cognitive spectrum sharing scheme between primary macrocell and secondary small cells. Specifically, the small cells allocate their transmission power to maximize their total energy efficiency while respecting some interference constraints imposed by macrocell users. We solve this centralized optimization in two steps. First, assuming that the small-cell transmissions are noninterfering, the solution of this nonconvex optimization is characterized using a convex parametric approach. Using this characterization, we derive an algorithm based on Newton method, which converges to a global optimal solution. Second, when the small-cell transmissions are not necessarily orthogonal, we derive an algorithm, which converges at least to a local optimum, using the minorization-maximization principle and Newton method. Through simulations, we validate the convergence of these algorithms and compare their performance with existing schemes. We also analyze the effects of the interference and of the number of users on the energy efficiency.

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Section: Numerical Resultsmentioning

confidence: 99%

“…We can identify (21), (24), (26), and (28) as the Karush-Kuhn-Tucker (KKT) optimality conditions [13] of (P x ) with x = ( w, θ). Since (P x ) is convex, these KKT conditions are sufficient to prove that p is also an optimal solution of (P x ) [13].…”

Section: A Parametric Convex Programmingmentioning

confidence: 99%

Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks

Ramamonjison

Bhargava

2015

IEEE Trans. Wireless Commun.

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“…• denotes the geographic distance between the two location parameters; and, r j is the cellular radius of tower c j , i.e., the maximum distance between the actual tower position c j and the farthest corner of its Voronoi tessellation cell [28] as illustrated in Fig. 7.…”

Section: Homogeneous Quantization Of Locationsmentioning

confidence: 99%

Complete trajectory reconstruction from sparse mobile phone data

et al. 2019

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Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

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“…Now we divide the wireless network into various smaller unit called as Voronoi cells (Voronoi diagram) . As we split the wireless network into smaller cells it is easy to find out the strength of similar attacks within the voronoi cells [15] . We focus our attention to find the gradient of the similar attacks with respect to each other in different frequency signals in a single voronoi cellular topology of wireless network .Assuming Q 1 , Q 2 , Q 3 respectively are the different signals and are infected by similar attacks R 1 , R 2 , R 3 respectively with in a single voronoi unit cell.…”

Section: (A) Attack Modelsmentioning

confidence: 99%

Tracing the Abnormal Behavior of Malicious Nodes in MANET

Karjee

Banerjee²

2008

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing

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This paper introduces a new technique for tracing the malicious nodes in Mobile Adhoc Network (MANET) for wireless communication .As the Mobile hosts are free to move any where and can change the wireless topology frequently, each node of MANET are much more vulnerable to insider attacks specially under noise and external attacks due to lack of permanent infrastructure, limited resources, central administrator etc. Here in this paper, we have done an investigation and mathematical analysis based upon the detection of malicious node with attacks modeling .We propose a scanning procedure and security measures for the multihop wireless network after diagnosis the abnormal behavior of malicious node and verify the physical presence of attacks strategy in wireless network.

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Cellular network as a multiplicatively weighted voronoi diagram

Cited by 8 publications

References 9 publications

Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks

Energy Efficiency Maximization Framework in Cognitive Downlink Two-Tier Networks

Complete trajectory reconstruction from sparse mobile phone data

Tracing the Abnormal Behavior of Malicious Nodes in MANET

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