论文标题
如何最大程度地减少多源状态更新系统中的加权总和AOI：OMA或NOMA？
How to Minimize the Weighted Sum AoI in Multi-Source Status Update Systems: OMA or NOMA?
论文作者
论文摘要
在本文中，研究了多源状态更新通信系统中加权总和信息年龄（AOI）的最小化。多个独立的来源以时间间隙的方式将更新数据包发送到公共目的地节点。不同的多个访问方案，即正交多访问（OMA）和非正交多访问（NOMA），在此处通过块障碍的多个访问通道（MAC）利用。限制了马尔可夫决策过程（CMDP）问题，以描述考虑这两个传输方案的AOI最小化问题。 Lagrangian方法用于将CMDP问题转换为Markov决策过程（MDP）问题和相应的算法，以得出得出功率分配策略。另一方面，对于未知环境的情况，考虑了两种在线加强学习方法，考虑了两种多个访问方案，以实现近乎最佳的年龄绩效。数值模拟与固定电力传输策略相比，根据加权总和AOI来验证拟议策略的改进，并说明在包装较大的数据包大小的情况下，Noma更有利。
In this paper, the minimization of the weighted sum average age of information (AoI) in a multi-source status update communication system is studied. Multiple independent sources send update packets to a common destination node in a time-slotted manner under the limit of maximum retransmission rounds. Different multiple access schemes, i.e., orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are exploited here over a block-fading multiple access channel (MAC). Constrained Markov decision process (CMDP) problems are formulated to describe the AoI minimization problems considering both transmission schemes. The Lagrangian method is utilised to convert CMDP problems to unconstraint Markov decision process (MDP) problems and corresponding algorithms to derive the power allocation policies are obtained. On the other hand, for the case of unknown environments, two online reinforcement learning approaches considering both multiple access schemes are proposed to achieve near-optimal age performance. Numerical simulations validate the improvement of the proposed policy in terms of weighted sum AoI compared to the fixed power transmission policy, and illustrate that NOMA is more favorable in case of larger packet size.
