Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning

This article studies the problem of online user grouping, scheduling, and power allocation for massive Internet of Things (IoT) access in beyond 5G networks using nonorthogonal multiple access (NOMA). NOMA has been identified as a promising technology to accommodate a large number of devices using a limited number of radio resources. In this work, the objective is to maximize the number of served devices while allocating their transmission powers such that their real-time requirements as well as their limited operating energy are respected. First, we formulate the problem as a mixed-integer nonlinear program (MINLP) that can be transformed to MILP for some special cases. Second, we study its NP-hardness in different cases. Then, by dividing the problem into multiple NOMA grouping and scheduling subproblems, an efficient online competitive algorithm is proposed to solve each subproblem. Next, we show how to use the proposed online algorithm as a black box and how to combine the obtained solutions to each subproblem in a reinforcement learning setting to obtain the power allocation for each NOMA group. Our analyses are supplemented by simulation results to illustrate the performance of the proposed algorithms in comparison to optimal and state-of-the-art methods.