工业无线共存环境的统计分析与预测建模

G. Shrestha, Kaleem Ahmad, U. Meier
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引用次数: 2

摘要

通常,认知无线电系统要么在传输前感知信道,要么定期执行此任务,以保持对操作环境的了解。然而,在认知系统的传输过程中,被感知为“空闲”的信道可能会变得繁忙,从而导致有害的碰撞和二次用户数据传输的不必要中断。作为一种解决方案,基于预测的方法已经被提出,并在模拟环境中显示出令人满意的结果。然而,对实时、动态、共存的环境进行建模需要使用实时演示进行调查。本文以工业共存环境为研究对象,阐述了预测模型的选择及其参数估计准则。在此基础上,利用CC2500 TRX和MSP430 μC处理器搭建了实时测试平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical analysis and predictive modeling of industrial wireless coexisting environments
Typically, cognitive radio systems either sense the channel just before transmission or perform this task periodically in order to remain aware about the operational environment. However, a channel sensed as `free' can become busy during the transmission of the cognitive system resulting in harmful collisions and unnecessary interruptions in the secondary user data transmission. As a solution, predictive based approaches has been proposed and has shown promising results in simulated environments. However, modeling real-time, dynamic, coexisting environments demand investigation with real-time demonstrators. This paper investigates industrial coexisting environments and illustrates the prediction model selection and its parameter estimation criteria. Based on the investigation a real-time testbed is implemented using a CC2500 TRX and MSP430 μC based platform.
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