Cooperative sensing utilizes multiple receivers dispersed across different locations, capitalizing on the advantages of multiple antennas and spatial diversity gain. This mechanism is crucial for monitoring the availability of licensed spectrum for secondary use when free from primary users. However, the efficacy of cooperative sensing relies heavily on the flawless transmission of raw data from cooperating receivers to a fusion center, a condition that may not always be fulfilled in real-world scenarios. This study investigates cooperative sensing in the context where the raw data is compromised by errors introduced during transmission, attributable to a relatively high bit error rate (BER). Consequently, the data received at the fusion center becomes incomplete and contaminated. Conventional multiantenna detectors are not adequately designed to handle such situations. To overcome this, we introduce the missing-data $t$ -distribution generalized likelihood ratio test ( $mt$ GLRT) detectors to manage such problematic data at the fusion center. The structured covariance matrices are estimated from this problematic data. Efficient optimization algorithms using the generalized expectation-maximization (GEM) method are developed accordingly. Numerical experiments corroborate the robustness of the proposed cooperative sensing methods.

中文翻译：

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针对不完整和污染数据的鲁棒协作传感方法


协作传感利用分散在不同位置的多个接收器，充分利用多个天线和空间分集增益的优势。该机制对于监控主要用户空闲时二次使用的许可频谱的可用性至关重要。然而，协作传感的功效在很大程度上依赖于原始数据从协作接收器到融合中心的完美传输，这一条件在现实场景中可能并不总是能够满足。这项研究调查了原始数据因传输过程中引入的错误而受到损害的情况下的协作感知，这些错误可归因于相对较高的误码率 (BER)。因此，融合中心接收到的数据变得不完整且受到污染。传统的多天线检测器的设计不足以处理这种情况。为了克服这个问题，我们引入了缺失数据$t$分布广义似然比测试（$mt$GLRT）检测器来管理融合中心的此类有问题的数据。结构化协方差矩阵是根据这些有问题的数据来估计的。相应地开发了使用广义期望最大化（GEM）方法的有效优化算法。数值实验证实了所提出的协作传感方法的鲁棒性。