Abstract: Cooperative spectrum sensing is regarded as a key technology to tackle the challenges such as hidden terminal problem in local spectrum sensing of cognitive radio networks. In this paper, the cooperation strategy based on data fusion is chosen for better collective sensing performance, in which all cooperative users send their own local results of energy detection to the fusion centre for linear data combination and final decision. As the main focus of this work, the optimization of linear data fusion is investigated. Specifically, the optimal weight vectors for all users are derived under Neyman-Pearson (N-P) and Bayesian criteria, respectively. Monte Carlo simulations and numerical results are given under the assumption that the sensing channels follow Suzuki distribution. Obtained results demonstrate that the two optimal fusion schemes under N-P criterion, MDC and NDC have the similar detection performance, and they both outperform three other generally used schemes, including EGC, SC and MRC. Further, the optimal fusion scheme BAY, which is derived under Bayesian criterion, is verified to be more reliable than other schemes.