The Complexity-Performance Tradeoff in Resource Allocation for URLLC Exploiting Dynamic CSI

The challenging applications envisioned for the future Internet of Things networks are making it urgent to develop fast and scalable resource allocation algorithms able to meet the stringent reliability and latency constraints typical of the ultra-reliable, low-latency communications (URLLCs). However, there is an inherent tradeoff between complexity and performance to be addressed: sophisticated resource allocation methods providing optimized spectrum utilization are challenged by the scale of applications and the concomitant stringent latency constraints. Whether nontrivial resource allocation approaches can be successfully applied in large-scale network instances is still an open question that this article aims to address. More specifically, we consider a scenario in which channel-state information (CSI) is used to improve spectrum allocation in a radio environment that experiences channel time correlation. Channel correlation allows the usage of CSI for longer time before an update, thus lowering the overhead burden. Following this intuition, we propose a dynamic pilot transmission allocation scheme in order to adaptively tune the CSI age. We systematically analyze the improvement of this approach applied to a sophisticated, recently introduced graph-based resource allocation method that we extend here to account for CSI. The results show that even in very dense networks and accounting for the higher computational time of the graph-based approach, this algorithm is able to improve spectrum efficiency by over 12% as compared to a greedy heuristic, and that dynamic pilot transmissions allocation can further boost its performance in terms of fairness, while concomitantly further increase spectrum efficiency of 3%-5%.