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Exploiting Interference Diversity for Event-Based Spectrum Sensing

机译:利用基于事件的频谱感测的干扰分集

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Spectrum sensing is a core problem in cognitive radio. Detecting the presence/absence of very weak primary users with a single antenna can be very difficult. Environmental uncertainties result in SNR walls that detectors cannot overcome. Multiple-antenna approaches show some potential gains, but we show here that single-user multiple-antenna detection still must suffer from an SNR wall. The reason is that the real-world uncertainty in noise is dominated by the potential presence of an unknown number of low-power and time-varying interference sources in the external environment. Traditional collaborative spectrum sensing attempts to use the shadowing/multipath diversity across different users to boost the reliability of detection. We show that there is another kind of diversity that is also available: interference diversity. This captures the fact that low-powered interference sources are local to individual users whereas the primary user has a global footprint. To exploit this diversity, we must shift our perspective from existence-based detection (whether the primary is present or not) to event-based detection (whether the primary has turned off or on).
机译:光谱感测是认知无线电中的核心问题。用单个天线检测非常弱的主要用户的存在/不存在可能非常困难。环境不确定性导致探测器无法克服的SNR墙壁。多天线方法显示出一些潜在的收益,但我们在此显示单用户多天线检测仍然必须遭受SNR墙壁。原因是噪声中的实际不确定性是由外部环境中未知数量的低功耗和时变干源的潜在存在的主导。传统的协作频谱感测尝试在不同用户中使用阴影/多径多样性来提高检测的可靠性。我们表明还有另一种多样性也可用:干扰分集。这捕获了低功耗的干扰源是单个用户本地的事实,而主用户具有全局占用空间。为了利用这种多样性,我们必须从基于生存的检测(主要存在或不存在)转移到基于事件的检测(主要是否已关闭或打开)。

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