Age-of-Information (AoI) is an application layer metric that has been widely adopted to quantify the information freshness of each information source. However, few works address the impact of probabilistic transmission failures on satisfying the harsh AoI requirement of each source, which is of critical importance in a great number of wireless-powered real-time applications. In this paper, we investigate the transmission scheduling problem of maximizing throughput over wireless channels under different time-average AoI requirements for heterogeneous information sources. When the channel reliability for each source is known as prior, the global optimal transmission scheduling policy is proposed. Moreover, when channel reliabilities are unknown, it is modeled as an AoI-constrained Multi-Armed Bandit (MAB) problem. Then a learning algorithm that meets the AoI requirement with probability 1 and incurs up to O(K\sqrt{T\log T}) accumulated regret is proposed, where K is the number of arms/information sources, and T is the time horizon. Numerical results show that the accumulated regret of our learning algorithm is strictly bounded by K\sqrt{T\log T} and outperforms the AoI-constraint-aware baseline, and the AoI requirement of every source is robustly satisfied.
翻译:信息时代(AoI)是一个应用层指标,已被广泛采用,以量化每个信息来源的信息新鲜度。然而,很少有作品处理概率性传输失败对满足每个源的严酷AoI要求的影响,这对大量无线动力实时应用程序至关重要。在本文中,我们调查了在不同平均时间平均AoI要求下通过无线频道对多种信息来源的最大吞吐量的传输时间安排问题。当每个源的频道可靠性被事先知道时,就提出了全球最佳传输时间安排政策。此外,当频道恢复能力未知时,它被建为AoI所限制的多装甲土匪(MAB)问题模型。然后,我们提出了一种符合AoI要求的学习算法,概率为1,并导致O(K\qrt{T\log T} 积累的遗憾,K是武器/信息来源的数量,T是时间范围。数量显示我们学习算法累积的遗憾被K\srestrction-stractionAstraprial-strogrogy 和Astria-stal-strigrom) 和Astal-staltistration-Itram-Itram和Astration-Itram-Itramtramst-st-st-st-Itram)的源。