In 5G and beyond communication systems, the notion of latency gets great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, Age of Information (AoI) metric, which can capture the freshness of the data, has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing the Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. In addition, we provide a low-complexity algorithm guaranteeing that the timely-throughput constraint is satisfied. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Simulation results show the convergence of the algorithms for different values of the weighted factor and the trade-off between the AoI and the timely throughput.

翻译：在5G及以后通信系统中，时延作为衡量实时通信需求的关键指标，在无线连接领域获得了极大关注。然而研究表明，对于具有数据新鲜度需求的应用，仅依赖时延指标可能无法有效处理。近年来，能够捕获数据新鲜度的信息年龄（Age of Information, AoI）度量引起了广泛关注。本文考虑混合流量场景，其中包含两类时间敏感型用户：一类具有截止时间约束，另一类面向AoI优化。为制定高效调度策略，我们提出一种新颖的优化问题建模，旨在满足及时吞吐量约束的同时最小化平均AoI。该问题被建模为约束马尔可夫决策过程（Constrained Markov Decision Process, CMDP）。通过利用李雅普诺夫优化理论，我们将约束问题松弛为无约束马尔可夫决策过程（Markov Decision Process, MDP）问题，并证明该问题可通过应用具有最优性保证的后向动态规划算法按帧求解。此外，我们提出一种低复杂度算法，确保及时吞吐量约束得到满足。仿真结果表明，该算法在最小化平均AoI的同时，能有效满足及时吞吐量约束。仿真结果进一步展示了算法在不同权重因子下的收敛性，以及AoI与及时吞吐量之间的权衡关系。