Non-Orthogonal Multiple Access (NOMA) is classified into grant-based (GB) RA and grant-free (GF) RA in 5G systems. GF NOMA RA allows UEs to directly transmit data packet over uplink resources without grant information so that the signaling overhead is substantially reduced. This approach is suitable for small-size data packet IoT services. However, power collision interference resulting from uncoordinated resource selection leads to serious transmission failure in massive MTC scenarios. As a result, the number of transmissions and access delay are sharply increased. In this article, we make the best of the advantages of both GB RA and GF RA and propose a hybrid grant (HG) NOMA RA scheme to address the signaling overhead and power collision issues. The objective of the proposed scheme is to achieve an acceptable tradeoff among signaling overhead, access failure probability and access delay. The UE selects the access resource without uplink grant, and the BS assists to avoid inter-NOMA power collision by limited interference resolution (IR) signaling. To comprehensively analyze the performance, the closed-form expression of outage probability is derived by adopting the order statistic theory and the sum of independent exponential distributions, and the maximum number of transmissions and access delay are theoretical analyzed. A Monte Carlo simulation framework is built to verify the theoretical analysis. Finally, the analytical and simulation results show that HG NOMA RA outperforms GB NOMA RA and GF NOMA RA in massive access scenarios.

Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge‐weight‐based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.

Information spreading on multiplex complex networks has been widely concerned. In reality, the constraints of inelastic resources, such as energy, time, money, and an individual’s restrict contact capacities, can affect the information spreading process. In this paper, we propose an information spreading model with limited contact capacity on the two-layered multiplex network. In order to quantitatively study the information spreading, we adopt an edge-based compartmental theory. We find that increasing the contact capacity facilitates information spreading. If the multiplex networks with strong heterogeneous degree distributions, the information outbreak size grows continuously with the transmission probability. However, when the information spreads on multiplex networks with homogeneous or heterogeneous degree distributions, there exists a crossover phenomenon, in which the information outbreak size versus the transmission probability can grow continuously or discontinuously. Our theory agrees well with the numerical simulations.

In previous link prediction researches, most scholars evaluate the influence of endpoints by the degree or H-index of endpoints, resulting in limited prediction accuracy. Through abundant investigations, we can evaluate the influence of endpoints accurately by the hybrid influence of neighbor nodes. Meanwhile, we calculate the hybrid influence of neighbors (HIN) by the average values of degree and H-index. In the paper, we conceive a HIN model. Large-scale experiments on 12 real datasets indicate that the conceived methods can significantly enhance the accuracy of link prediction.