期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 16, 期 7, 页码 4547-4561出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2017.2699966
关键词
LTE-Unlicensed; user mobility; stable marriage problem; random path to stability; matching theory
资金
- U.S. National Science Foundation [CNS-1702850, CNS-1646607, ECCS-1547201, CCF-1456921, CMMI-1434789, CNS-1443917, ECCS-1405121, CNS-1343361, CNS-1350230]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1613661] Funding Source: National Science Foundation
LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users' performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale-Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据