具有高效频谱分配和QoS保证的认知无线电辅助车载自组织网络

E. C. Joy, Sijing Zhang, E. Liu, Efor E. Theresa, E. Eze
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引用次数: 13

摘要

面向车联网的各种应用(安全和非安全相关业务)的需求日益增长,无疑会给车联网通信网络带来频谱资源短缺的挑战。为了解决这一问题,本文提出了一种新的自适应CR车辆网络(ACREVNET)框架。为了避免频谱感知过程中产生的巨大开销,我们提出了一种新的CR自适应频谱感知(CRASS)方案,该方案能够有效地降低频谱感知成本并提高感知性能。在ACREVNET框架中,我们应用纳什讨价还价解决方案(NBS)的概念来保证频谱资源分配的公平性,并提出了一种广义非对称NBS (GNNBS)来执行非对称认知小区间频谱分配。仿真结果清楚地表明,所提出的方案可以利用CR技术为车载通信获取额外的频谱资源,并显著降低消息传输延迟和阻塞概率。
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Cognitive radio aided Vehicular ad-hoc networks with efficient spectrum allocation and QoS guarantee
The increasing demand of diverse vehicular network oriented applications (both safety and non-safety related services would undoubtedly lead to shortage of spectral resource challenge for V2V communication networks. In order to resolve this issue, a novel Adaptive CR Enabled Vehicular NETwork (ACREVNET) framework is proposed in this paper. To avoid heavy overhead usually incurred during spectrum sensing, we developed a novel CR adaptive spectrum sensing (CRASS) scheme that can reduce the spectrum sensing cost and improve sensing performance effectively. We also applied the concept of Nash Bargaining Solution (NBS) to guarantee fairness in spectral resources allocation and proposed a generalized non-symmetric NBS (GNNBS) to perform a non-symmetric cognitive inter-cell spectrum allocation in the proposed ACREVNET framework. Simulation results clearly show that the proposed schemes can acquire additional spectral resource for vehicular communication by applying CR technology, and reduce the message transmission delay and blocking probability significantly.
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