协作认知无线网络中联合中继选择与资源分配的联盟图博弈

Lanjie Zhai, Hong Ji, Xi Li, Yiwen Tang
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引用次数: 15

摘要

认知无线网络(CRN)中二次用户间的合作中继技术在提高频谱利用率和系统公平性方面具有显著的性能提升。研究了OFDMA下行认知中继网络中的中继选择和资源分配问题。该优化问题的目标是最大化系统吞吐量和系统公平性,这是通过我们提出的不可转移效用联盟图博弈算法(NTU-CGGA)来实现的。在该算法中,具有更多可用通道的单元可以通过形成有向树图来帮助具有较少可用通道的单元提高它们在吞吐量和公平性方面的效用。采用合并分裂规则,根据单元的频谱可用性和流量需求格式化联盟图。因此,它可以有效地利用系统的空间分集和频率分集。仿真结果表明,NTU-CGGA在不降低公平性的前提下显著提高了系统吞吐量,与现有算法相比具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Coalition Graph Game for joint relay selection and resource allocation in cooperative cognitive radio networks
Cooperative relaying technology among secondary users (SUs) in cognitive radio networks (CRN) is shown to yield a significant performance improvement, such as improving spectrum utilization as well as system fairness in CRN. This paper investigates the problem of the relay selection and resource allocation in a downlink OFDMA cognitive relay network. The objective of this optimization problem is to maximize both system throughput and system fairness, which is achieved through our proposed non-transferable utility coalition graph game algorithm (NTU-CGGA). In this algorithm, the SUs with more available channels can help SUs with less available channels to improve their utility in terms of the throughput and fairness by forming a directed tree graph. The coalition graph is formatted according to spectrum availability and traffic demands of SUs by using merge-split rule. So it can effectively exploit both space and frequency diversity of the system. Simulation results show that, NTU-CGGA significantly improves system throughput while not reducing the fairness level, which has a better performance comparing with other existing algorithms.
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