基于功率阈值和人工智能的认知无线电频谱感知

Z. Abbood, Noor Alhuda F. Abbas, Basma Makki
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引用次数: 0

摘要

本文的目的是采取一种预防性的方法来预测主用户(PU)在整个仿真期间在白频谱上的传输状态。我们打算通过精确的频谱感知切换来减少计算仿真预算以消除用户干扰。我们开发PU行为,使用时间等待估计方法,给出频带占用时间间隔。建议的策略还可以减少与灵活客户相关的风险,并适应不断变化的渠道。实时应用将使用人工神经网络范围共享,减少传输延迟,而高吞吐量应用将使用PTHD范围共享。由于每个传输方案后的噪声和遮挡效应,它们在信道中的接近程度会影响距离检测。这一发现与已发表的调查结果一致。大多数距离检测系统将频率测试结果与操作通道的控制范围厚度进行比较。本文通过开发一种具有主动时间估计的噪声无关传感方法来满足设计目标。该试验以较低的成本和延迟完成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectrum Sensing Utilizing Power Threshold and Artificial Intelligence in Cognitive Radio
The purpose of this paper was to take a preventative approach to predict the transmission status of the primary user (PU) on the white spectrum throughout the duration of the simulation. We intend to reduce the computational simulation budget to eliminate user interference by precise spectrum sensing handoff. We develop PU behaviors to use a time waiting estimate methodology that gives band occupancy time intervals. The proposed strategy may also lessen risks associated with flexible clients and adapt to changing channels. Real-time applications will use ANN range sharing, which reduces transmission delay, while high-throughput applications will use PTHD range sharing. Since noise as well as obscuring effects after each transmission plan, their proximity in the channel can influence range detection. This finding is in line with published investigations. Most range detection systems compare frequency test results with the operating channel's control scope thickness. The paper met design objectives by developing a noise-independent sensing methodology with proactive time estimation. The trial is accomplished with reduced cost and latency.
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