Demand-aware traffic cooperation for self-organizing cognitive small-cell networks

Changhua Yao, Lei Zhu, Yongxing Jia and Lei Wang

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 1, 1550147718817289

Abstract: This article investigates the problem of efficient spectrum access for traffic demands of self-organizing cognitive small-cell networks, using the coalitional game approach. In particular, we propose a novel spectrum and time two-dimensional Traffic Cooperation Coalitional Game model which aims to improve the network throughput. The main motivation is to complete the data traffics of users, and the main idea is to make use of spectrum resource efficiently by reducing mutual interference in the spectrum dimension and considering cooperative data transmission in the time dimension at the same time. With the approach of coalition formation, compared with the traditional binary order in most existing coalition formation algorithms, the proposed functional order indicates a more flexibly preferring action which is a functional value determined by the environment information. To solve the distributed self-organizing traffic cooperation coalition formation problem, we propose three coalition formation algorithms: the first one is the Binary Preferring Traffic Cooperation Coalition Formation Algorithm based on the traditional Binary Preferring order; the second one is the Best Selection Traffic Cooperation Coalition Formation Algorithm based on the functional Best Selection order to improve the converging speed; and the third one is the Probabilistic Decision Traffic Cooperation Coalition Formation Algorithm based on the functional Probabilistic Decision order to improve the performance of the formed coalition. The proposed three algorithms are proved to converge to Nash-stable coalition structure. Simulation results verify the theoretic analysis and the proposed approaches.

Keywords: Demand-aware; cognitive radio; small cell; coalitional game (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:1:p:1550147718817289

DOI: 10.1177/1550147718817289

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