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http://dx.doi.org/10.3837/tiis.2019.02.002

Hypergraph Game Theoretic Solutions for Load Aware Dynamic Access of Ultra-dense Small Cell Networks  

Zhu, Xucheng (Army Engineering University of PLA College of Communication Engineering)
Xu, Yuhua (Army Engineering University of PLA College of Communication Engineering)
Liu, Xin (Guilin University of Technology)
Zhang, Yuli (Army Engineering University of PLA College of Communication Engineering)
Sun, Youming (National Digital Switching System Engineering &Technological Research Center)
Du, Zhiyong (National University of Defense Technology)
Liu, Dianxiong (Army Engineering University of PLA College of Communication Engineering)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.2, 2019 , pp. 494-513 More about this Journal
Abstract
A multi-channel access problem based on hypergraph model in ultra-dense small cell networks is studied in this paper. Due to the hyper-dense deployment of samll cells and the low-powered equipment, cumulative interference becomes an important problem besides the direct interference. The traditional binary interference model cannot capture the complicated interference relationship. In order to overcome this shortcoming, we use the hypergraph model to describe the cumulative interference relation among small cells. We formulate the multi-channel access problem based on hypergraph as two local altruistic games. The first game aims at minimizing the protocol MAC layer interference, which requires less information exchange and can converge faster. The second game aims at minimizing the physical layer interference. It needs more information interaction and converges slower, obtaining better performance. The two modeled games are both proved to be exact potential games, which admit at least one pure Nash Equilibrium (NE). To provide information exchange and reduce convergecne time, a cloud-based centralized-distributed algorithm is designed. Simulation results show that the proposed hypergraph models are both superior to the existing binary models and show the pros and cons of the two methods in different aspects.
Keywords
Hypergraph interference model; Ultra-dense small cell network; Centralized-distributed cloud learning framework; Heterogeneous demand;
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