Mitigating User Identification Errors in Resource Optimization for Grant-Free Random Access

In grant-free random access, a key question is how devices should utilize resources without coordination. One standard solution to this problem are strategies where devices randomly select time-slots based on an optimized stochastic allocation rule. However, the optimization of this allocation rule requires accurate knowledge of which devices have been active in previous frames. As user identification algorithms are subject to errors, the expected throughput of the optimized allocation can be highly suboptimal. In this paper, we propose algorithms for optimization of device time-slot allocations that mitigate the impact of user identification errors. We show that when the activity distribution with and without errors is known, then our algorithm converges with probability one to a stationary point. When the activity distributions are not available, we introduce new theoretically-motivated heuristics which significantly improve the expected throughput over existing algorithms and approach the performance when errors are not present.