NOMA-Assisted Cognitive Short-Packet Communication with Node Mobility and Imperfect Channel Estimation

To address the high-performance requirements of Internet of Things (IoT) application scenarios, we design a non-orthogonal multiple access (NOMA) assisted cognitive short packet communication system in this paper, in which the primary user is a mobile node and the secondary network shares the spectrum of primary network for transmission subject to transmit power constraints. To characterize the performance of the proposed system, the average block error rate (BLER) and throughput performance of the secondary users are derived and analyzed, and the theoretical results are verified using Monte Carlo simulations. To facilitate the performance improvement of network communication, the analysis and insights on the node's mobility and imperfect channel estimation are provided. The results show that both node's mobility and imperfect channel estimation result in the degradation of system performance. In addition, our examination shows that the increase in distance between primary and secondary networks indeed reduces interference and improves performance. Interestingly, we further find that shorter blocklength enhances the throughput performance for a long-distance scenario between primary transmitter and secondary users. In contrast, the opposite result is obtained for the short-distance scenario, which means that short packet communication is prominent in our proposed system.