IEEE Transactions on Communications · 2019 · 140 citations · 36 references
BusinessCommunicationComputer EngineeringComputer NetworksOptimizationQueuing TheoryExtreme Value TheoryMarkovian Queueing ModelsNetwork Information TheoryNetwork Performance AnalysisQueue Length DistributionsQueueing TheoryDistribution Tail AnalysisLow-latency CommunicationLyapunov FunctionsLyapunov StabilityQueue Length Probability DistributionTransmission OptimizationUltra-reliable Low-latency CommunicationWireless Network Models
Age of Information has been introduced for ultra‑reliable low‑latency communications, yet most studies focus on its average value, which cannot capture rare but critical extreme AoI events. This work seeks to enhance vehicular URLLC by characterizing and controlling the tail of the AoI distribution rather than its mean. The authors formulate a transmission‑power minimization problem under probabilistic AoI constraints for deterministic and Markovian traffic, introduce a novel mapping between AoI and queue‑related distributions, and solve it using extreme‑value theory and Lyapunov optimization for both long and short packets. Simulations demonstrate a more than two‑fold reduction in the AoI tail compared to a baseline based on maximum queue length, while revealing a trade‑off between arrival rate and AoI.
While the notion of age of information (AoI) has recently been proposed for analyzing ultra-reliable low-latency communications (URLLC), most of the existing works have focused on the average AoI measure. Designing a wireless network based on average AoI will fail to characterize the performance of URLLC systems, as it cannot account for extreme AoI events, occurring with very low probabilities. In contrast, this paper goes beyond the average AoI to improve URLLC in a vehicular communication network by characterizing and controlling the AoI tail distribution. In particular, the transmission power minimization problem is studied under stringent URLLC constraints in terms of probabilistic AoI for both deterministic and Markovian traffic arrivals. Accordingly, an efficient novel mapping between AoI and queue-related distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem considering both long and short packets transmissions. Simulation results show over a two-fold improvement, in shortening the AoI distribution tail, versus a baseline that models the maximum queue length distribution, in addition to a tradeoff between arrival rate and AoI.
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A tutorial on spectral clustering
Ulrike von Luxburg · Statistics and Computing · 2007 · 10.3K citations · Full text