Delay Minimization for Massive Internet of Things with Non-Orthogonal Multiple Access

Non-Orthogonal Multiple Access (NOMA) provides potential solutions for the stringent requirements of the Internet of Things (IoT) on low latency and high reliability. In this paper, we jointly consider user scheduling and power control to investigate the access delay minimization problem (ADMP) for the uplink NOMA networks with massive IoT devices. Specifically, the ADMP is formulated as a mixed-integer and non-convex programming problem with the objective to minimize the maximum access delay of all devices under individual data transmission demand. We prove that the ADMP is NP-hard. To tackle this hard problem, we divide it into two subproblems, i.e., the user scheduling subproblem (USP) and the power control subproblem (PCP), and then propose an efficient algorithm to solve them in an iterative manner. In particular, the USP is recast as a K-CUT problem and solved by a graph-based method. For the PCP, we devise an iterative algorithm to solve it optimally leveraging the standard interference function. Simulation results indicate that our algorithm has good convergence and can significantly reduce the access delay in comparison with other schemes.