2020/08/23 by Christina Chaccour, Walid Saad, Chaccour, Christina +1 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Age of Information Optimization #IoT Networks and Protocols #Dark Matter and Cosmic Phenomena
paper · pdf · doi:10.48550/arxiv.2008.09959
Guaranteeing fresh and reliable information for augmented reality (AR)\nservices is a key challenge to enable a real-time experience and sustain a high\nquality of physical experience (QoPE) for the users. In this paper, a terahertz\n(THz) cellular network is used to exchange rate-hungry AR content. For this\nnetwork, guaranteeing an instantaneous low peak age of information (PAoI) is\nnecessary to overcome the uncertainty stemming from the THz channel. In\nparticular, a novel economic concept, namely, the risk of ruin is proposed to\nexamine the probability of occurrence of rare, but extremely high PAoI that can\njeopardize the operation of the AR service. To assess the severity of these\nhazards, the cumulative distribution function (CDF) of the PAoI is derived for\ntwo different scheduling policies. This CDF is then used to find the\nprobability of maximum severity of ruin PAoI. Furthermore, to provide long term\ninsights about the AR content's age, the average PAoI of the overall system is\nalso derived. Simulation results show that an increase in the number of users\nwill positively impact the PAoI in both the expected and worst-case scenarios.\nMeanwhile, an increase in the bandwidth reduces the average PAoI but leads to a\ndecline in the severity of ruin performance. The results also show that a\nsystem with preemptive last come first served (LCFS) queues of limited size\nbuffers have a better ruin performance (12% increase in the probability of\nguaranteeing a less severe PAoI while increasing the number of users), whereas\nfirst come first served (FCFS) queues of limited buffers lead to a better\naverage PAoI performance (45% lower PAoI as we increase the bandwidth).\n