Graph Domination, Coloring and Cliques in Telecommunications

Balasundaram, Balabhaskar; Butenko, Sergiy

doi:10.1007/978-0-387-30165-5_30

Cited by 67 publications

(41 citation statements)

References 110 publications

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“…So to reduce f max it is necessary to schedule them according to their constraints with the last maximum stable that is {5, 6}. This leads to the scheduling (8,7). The resulting assignment is given in Table 3.…”

Section: Illustrationmentioning

confidence: 98%

“…Indeed, let's consider for example the cells 6, 7 and 8. For the two scheduling (a) (6,7,8) and (b) (6,8,7), Table 2 shows the respective assignment tables, say x (a) and x (b) as denoted in Section 2: x if = 1(resp. 0 not mentioned) if the frequency f ∈ {1, 2, 3, 4, 5} is (resp.…”

Section: Illustrationmentioning

confidence: 99%

“…Table 2. Frequencies assignment to cells 6, 7 and 8 according to the scheduling (6,7,8) and (6,8,7). f max = 5 (resp.…”

Section: Illustrationmentioning

confidence: 99%

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Frequencies Assignment in Cellular Networks

Sakho

2015

Intelligent Information and Database Systems

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Abstract. With the limited number of communication frequencies and the increasing number of users, the problem of communication frequencies assignment without interference is more than ever at the heart of the development of cellular networks. This paper reports a heuristic assignment based on the scheduling of the cells and the scheduling of the maximum stables of the dependency graph of the cells network. The purpose of the maximum stables scheduling is to assign without interference a maximum number of frequencies to cells while the purpose of the cells scheduling is to minimize the number of the used frequencies. Therefore aim of the heuristic is to satisfy a maximum number of connection requests with a minimum number of frequencies. The heuristic is implemented and its performance evaluated for the well known network test Philadelphia-benchmark.

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Section: Illustrationmentioning

confidence: 98%

Section: Illustrationmentioning

confidence: 99%

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Frequencies Assignment in Cellular Networks

Sakho

2015

Intelligent Information and Database Systems

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“…Occasionally a k-club happens to be a kclique as well, in which special case it is called a k-clan. 4 Recently k-clubs have found interest and applications in many network oriented disciplines, such as telecommunication (Balasundaram and Butenko 2006), biology (Balasundaram et al 2005), genetics (Pasupuleti 2008), forensic data mining (Memon and Larsen 2006), web search (Terveen et al 1999), graph mining (Cavique et al 2009), and language processing (Miao and Berleant 2004;Gutiérrez et al 2011). …”

Section: The 2-clubs Of a Graph Or Networkmentioning

confidence: 99%

Close communities in social networks: boroughs and 2-clubs

Laan

Marx

Mokken

2016

Soc. Netw. Anal. Min.

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The structure of close communication, contacts and association in social networks is studied in the form of maximal subgraphs of diameter 2 (2-clubs), corresponding to three types of close communities: hamlets, social circles and coteries. The concept of borough of a graph is defined and introduced. Each borough is a chained union of 2-clubs of the network and any 2-club of the network belongs to one borough. Thus the set of boroughs of a network, together with the 2-clubs held by them, are shown to contain the structure of close communication in a network. Applications are given with examples from real world network data.

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“…To manage the externalities across interfering UE-SBS links, we propose the representation of the initial SBSs network as an interference graph [19]. In such an approach, the network topology shown in Figure 1.a is translated into the interference graph of Figure 1.b, in which the vertices are interfering SBSs and UEs and the weight of the corresponding edges (shown as red dashed lines) is the received interference signal strength ′ ) ≻m (i, η) and RSSI of other clique edges is non-increased then…”

Section: B Context-aware Ue-sbs Association Approachmentioning

confidence: 99%

Proactive user association in wireless small cell networks via collaborative filtering

Pantisano¹,

Bennis²,

Saad

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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Abstract-In this paper, we propose a proactive cell association framework for small cell networks, based on content recommendations. We focus on multimedia data services and characterize the user's quality of experience (QoE) in terms of mean opinion scores (MOSs) that accurately reflect characteristics of the wireless transmissions and data applications. Based on such information, we propose a collaborative filtering approach that enables the small base stations (SBSs) to exploit the correlation across similar user and application requirements to predict the QoE that a UE-SBS association delivers. By leveraging such knowledge, the SBSs ultimately devise better-informed user-cell associations and more precise bandwidth allocations. We model this problem as a matching game with externalities, propose a decentralized algorithm based interference graph theory, and discuss the matching stability conditions in the proposed approach. Simulation results show that the proposed solution yields more precise bandwidth allocations reaching up to 19% spectrum savings, compared to conventional cell association approaches.

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Graph Domination, Coloring and Cliques in Telecommunications

Cited by 67 publications

References 110 publications

Frequencies Assignment in Cellular Networks

Frequencies Assignment in Cellular Networks

Close communities in social networks: boroughs and 2-clubs

Proactive user association in wireless small cell networks via collaborative filtering

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