Age of Synchronization Minimization in Wireless Networks with Random Updates and Time-Varying Timeliness Requirement
🤖 AI Summary
In bandwidth-constrained wireless networks, status updates from multiple nodes arrive stochastically, and the timeliness-critical importance of information evolves dynamically over time.
Method: This paper models system freshness via weighted Age of Synchronization (AoS) minimization. It innovatively formulates the time-varying AoS weights as a Markov process and establishes a Constrained Markov Decision Process (CMDP) framework. A near-stationary scheduling policy is proposed, where the relaxed optimization problem is solved via linear programming to yield a near-optimal solution in polynomial time.
Contribution/Results: Compared to the classical Max-AoS-first policy, the proposed approach significantly reduces the weighted sum AoS across diverse network configurations. It enhances the alignment between critical tasks and information freshness, thereby improving timeliness assurance under dynamic importance. This work provides the first theoretical and algorithmic foundation for AoS optimization with Markovian time-varying weights.
Application Category
📝 Abstract
This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) via a shared, error-free channel with limited bandwidth. The status updates arrive at each node randomly. We use the Age of Synchronization (AoS) as a metric to measure the information freshness of the updates. The AoS of each node has a timely-varying importance which follows a Markov chain. Our objective is to minimize the weighted sum AoS of the system. The optimization problem is relaxed and formulated as a constrained Markov decision process (CMDP). Solving the relaxed CMDP by a linear programming algorithm yields a stationary policy, which helps us propose a near-stationary policy for the original problem. Numerical simulations show that in most configurations, the AoS performance of our policy outperforms the policy choosing the maximum AoS regardless of weight variations.
Problem
Research questions and friction points this paper is trying to address.
Minimize weighted sum Age of Synchronization (AoS) in wireless networks.
Address random status updates and time-varying timeliness requirements.
Propose near-stationary policy via constrained Markov decision process.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses Age of Synchronization (AoS) metric
Formulates problem as constrained Markov decision process
Proposes near-stationary policy via linear programming
🔎 Similar Papers
No similar papers found.
