Throughput Estimation with Noise Uncertainty for Cyclostationary Feature Detector in Cognitive Radio Network

M. M. Tantawy
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引用次数: 0

Abstract

Cognitive Radio Networks (CRNs) are recognized as the enabling technology for improving the future bandwidth utilization. In CRNs secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. The secondary users are required to sense the radio frequency environment. The lower the probability of false alarm, the more chances the channel can be reused and the higher the achievable throughput for the secondary network. The main contribution of this paper is to formulate the sensing-throughput-noise uncertainty tradeoff for cyclostationary feature detection. Computer simulations have shown that for a 1 MHz channel, when the sensing duration is 2% of total time, the spectrum will get 99% probability of detection regardless of 50% noise uncertainty.
认知无线网络循环平稳特征检测器的噪声不确定性吞吐量估计
认知无线网络(crn)被认为是提高未来带宽利用率的使能技术。在crn中,当主用户的频段目前未被使用时,允许辅助用户使用这些频段。辅助用户需要对射频环境进行感知。虚警概率越低,通道被重用的机会越大,辅助网络的可实现吞吐量也就越高。本文的主要贡献是制定了循环平稳特征检测的传感-吞吐量-噪声不确定性权衡。计算机模拟表明,对于1 MHz信道,当感知持续时间为总时间的2%时,频谱将获得99%的检测概率,而不考虑50%的噪声不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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