Abstract | ||
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Device-to-device D2D communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference among spectrum-sharing links is severer than that in traditional cellular networks, which motivates the adoption of interference cancelation IC techniques at the receivers. However, to date, how IC can affect the performance of D2D-enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of two IC methods, that is, unconditional IC and successive IC, in large-scale D2D-enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose an approach of stochastic equivalence of the interference, which converts the two-tier interference interference from the cellular tier and D2D tier to an equivalent single-tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of both cellular uplinks and D2D links in the networks where unconditional IC and successive IC are respectively applied. We demonstrate how these IC methods affect the network performance using both analytical and numerical results. Copyright © 2016 John Wiley & Sons, Ltd. |
Year | DOI | Venue |
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2016 | 10.1002/wcm.2712 | Wireless Communications and Mobile Computing |
Keywords | Field | DocType |
D2D communication,cellular network,interference cancelation,stochastic equivalence,stochastic geometry | Stochastic geometry,Expression (mathematics),Computer science,Equivalence (measure theory),Interference (wave propagation),Cellular network,Interference cancelation,Network performance,Distributed computing | Journal |
Volume | Issue | ISSN |
16 | 16 | 1530-8669 |
Citations | PageRank | References |
2 | 0.37 | 25 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Chuan Ma | 1 | 140 | 6.72 |
Weijie Wu | 2 | 112 | 13.30 |
Ying Cui | 3 | 377 | 38.02 |
Xinbing Wang | 4 | 2642 | 214.43 |