Resource Allocation for Covert Communication in D2D Content Sharing: A Matching Game Approach

Device-to-device (D2D) content sharing, as a promising solution to rapidly growing mobile data traffic, is facing serious security issues. Hence, how to ensure its security is challenging and meaningful work. Covert communication is regarded as an emerging and cutting-edge security technique for its higher level of security and less need for channel state information (CSI), and accordingly, has attracted wide attention. However, it is not easy to directly apply covert communication to D2D content sharing to ensure both security and efficiency. In this paper, a novel covert communication model is constructed in D2D content sharing scenario, where the co-channel interference (CCI) introduced by spectrum reusing is exploited as the cover of contents so that the contents are hidden from the warden. Then, we propose a secure and efficient resource allocation scheme to ensure both security and efficiency of D2D content sharing by addressing the following two issues: 1) In order to guarantee the robustness of our scheme, covert constraints are learned by analyzing the detection performance at the warden, i.e., the security of D2D content sharing is guaranteed even considering some extremely adverse environments. 2) The joint spectrum allocation and power control is modeled as a two-sided matching problem and then reformulated as the one-to-one matching game based on the principle of mutual benefit, in order to ensure the quality of service (QoS) requirements of both D2D pairs and cellular users. Then, covert constraints guaranteed resource allocation algorithm based on Gale- Shapley algorithm is proposed. Its properties such as stability, optimality, convergence, and complexity are analyzed. The extensive simulation results are provided to verify the theoretical analyses and demonstrate the efficiency of our proposed algorithm, which has at least 7.63% performance gain compared with some existing approaches and no more than 4.83% performance loss compared with the exhaustive search.