Optimal pricing strategy for resource allocation in 5G heterogeneous cellular networks. Issue 10 (19th June 2018)

• Record Type: Journal Article
• Authors: Chen, Xinyi; Chen, Yapeng; Liao, Wenlin; Zhou, Zhenyu; Gu, Bo; Wang, Xiaoyan
• Other Names: Pervaiz Haris guestEditor.; Imran Muhammad Ali guestEditor.; Mumtaz Shahid guestEditor.; Dulaimi Anwer‐al guestEditor.; Thomos Nikolaos guestEditor.
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 29:Issue 10(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-06-19
• Subjects: Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.3437
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Heterogeneous networks provide an effective solution to bridge the gap between enormous global traffic demands and limited spectrum resources. In this paper, we investigate the resource allocation problem in fifth‐generation heterogeneous cellular networks from an economic perspective. We consider the open access scenario where any cellular user equipment is allowed to access either the high‐power node or the low‐power node based on its individual payoff. The resource allocation problem is formulated as a pricing problem, which aims at maximizing the total revenue of the service providers under the user admission constraint. Then, the optimal pricing strategies for both the high‐power node and the low‐power node are developed based on the decisions of cellular user equipments. Simulation results demonstrate that the proposed pricing algorithm can achieve the maximum revenue for service providers under various scenarios. Abstract : In heterogeneous networks, we consider the open access scenario where any cellular user equipment (CUE) is allowed to access either the high‐power node (HPN) or the low‐power node (LPN). The resource allocation problem is formulated as a pricing problem, and the optimal pricing strategies for both the HPN and the LPN are developed based on the decisions of CUEs. Simulation results demonstrate that the proposed pricing algorithm can achieve the maximum revenue for service providers under various scenarios.