LAUSR

In this article, we propose an uplink cell-free Industrial Internet of Things (IIoT) framework to support a large number of devices with random data arrivals. By adopting nonorthogonal random pilots and the large-scale fading decoding technique, we derive the closed-form expression of the transmission capacity for each terminal. Considering different statistics of random data arrivals, we formulate a long-term stochastic optimization problem to maximize the minimum time average transmission success ratio (TATSR) through jointly determining the power control coefficients and the combining coefficients for each time period. We reformulate the long-term max–min problem into a sequence of subproblems to minimize the Lyapunov drift plus penalty in each time period. We approximate each mixed integer subproblem as a sigmoid optimization problem, and propose an iterative algorithm by the aid of quadratic transform-based fractional programming and the sequential convex programming to solve it. Simulation results show that our proposed scheme can boost the TATSR.