A Robust Cooperative Sensing Approach for Incomplete and Contaminated Data

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.