Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks

Abstract

While the notion of age of information (AoI) has recently been proposed for\nanalyzing ultra-reliable low-latency communications (URLLC), most of the\nexisting works have focused on the average AoI measure. Designing a wireless\nnetwork based on average AoI will fail to characterize the performance of URLLC\nsystems, as it cannot account for extreme AoI events, occurring with very low\nprobabilities. In contrast, this paper goes beyond the average AoI to improve\nURLLC in a vehicular communication network by characterizing and controlling\nthe AoI tail distribution. In particular, the transmission power minimization\nproblem is studied under stringent URLLC constraints in terms of probabilistic\nAoI for both deterministic and Markovian traffic arrivals. Accordingly, an\nefficient novel mapping between AoI and queue-related distributions is\nproposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization\ntechniques are adopted to formulate and solve the problem considering both long\nand short packets transmissions. Simulation results show over a two-fold\nimprovement, in shortening the AoI distribution tail, versus a baseline that\nmodels the maximum queue length distribution, in addition to a tradeoff between\narrival rate and AoI.\n