基于设备选择的认知聚类机器对机器网络性能分析

Mohammed A. M. Abdullah, Gaojie Chen, Mahmoud Alageli, A. Ikhlef, J. Chambers
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引用次数: 2

摘要

本文考虑基于集群的底层机器对机器(M2M)通信,提出了一种联合蜂窝用户设备(CUE)和M2M设备选择方案,以降低M2M通信的中断概率。我们导出了从选定的M2M设备到基站(BS)传输中断概率的解析封闭形式表达式。仿真结果表明,所提出的CUE和M2M选择方案实现了M2M通信的最低中断概率,对BS的干扰被限制在预定义的水平。混合双工BS通信可以根据残余自干扰的不同程度在半双工和全双工之间切换,以达到最佳性能。数值模拟验证了理论分析的准确性。本文提出的模型及其相关分析为这一重要领域的进一步工作铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Analysis of Cognitive Clustered Machine-to-Machine Networks with Device Selection
In this paper, we consider cluster-based underlay Machine-to-Machine (M2M) communications and propose a joint Cellular User Equipment (CUE) and M2M device selection scheme to reduce the outage probability of M2M communications. We derive an analytical closed-form expression for the outage probability of transmission from a selected M2M device to the Base Station (BS). Simulation results confirm that the proposed CUE and M2M selection scheme achieves the lowest outage probability for M2M communications with interference to the BS being limited by a pre-defined level. Hybrid duplex BS communication can be switched between half and full duplex according to different levels of residual self-interference to achieve the best performance. Numerical simulations are included to verify the accuracy of the theoretical analysis of the proposed scheme. The proposed model given in this paper and its related analysis pave the way for further work in this important area.
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