In a multi-user cognitive radio network (CRN), mobile or hidden secondary users (SUs) are present. Conventionally, such SUs utilize rigid threshold value (λ) for spectrum sensing (SS). When these SUs perform SS, they receive poor quality signals from primary users. This provokes erratic SS decisions, reduces the involvement of SUs in SS, diminishes the probability of detection ( P _d ) and raises miss detection ( P _m ) or false alarm of SUs. As a consequence of this, SUs are not able to participate in SS and allocation in emergency circumstances. To alleviate these problems, an adaptive λ can be utilized for mobile/hidden SUs during SS. Thus, a two-level λ optimization technique for SS has been proposed using a coalition formation approach to maintain co-operation among SUs. The proposed technique has been validated on Universal Software Radio Peripheral test-bed. Estimation of the probability of error ( P _e ), P _d , and P _m for mobile/hidden SUs with energy detection SS (EDSS) performed using a fixed λ and the two-level optimized λ. From these estimations, it has been observed that compared to the fixed λ EDSS, P _e and P _m is reduced remarkably, and P _d is enhanced for mobile/hidden SUs using the two-level optimized λ EDSS.

中文翻译：

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改善多用户CRN中移动/隐藏二级用户的性能

在多用户认知无线电网络（CRN）中，存在移动或隐藏的辅助用户（SU）。常规地，这样的SU将刚性阈值（λ）用于频谱感测（SS）。这些SU执行SS时，它们会收到来自主要用户的质量差的信号。这会激起SS决策的不稳定，减少SU在SS中的参与，降低检测的可能性（ P _d ）并提高未命中检测率（ P _m ）或SU的错误警报。因此，SU在紧急情况下无法参与SS和分配。为了减轻这些问题，可以将自适应λ用于SS期间的移动/隐藏SU。因此，已经提出了使用联盟形成方法来维持SU之间的协作的SS的两级λ最优化技术。所提出的技术已在通用软件无线电外围测试台上得到验证。估计错误概率（ P _e ），P _d 和P 对于使用固定λ和两级优化λ进行能量检测SS（EDSS）的移动/隐藏SU的_m。根据这些估算，可以发现与固定的λEDSS相比，P _e和P _m显着减小P 使用两级优化的λEDSS对移动/隐藏式SU增强_d。