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Energy-Optimal Data Compression and Transmission Under AoI Constraints in IRS-Aided Industrial Systems
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DOI：10.1109/tgcn.2026.3707922.png)
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Abstract
En 中文
Energy consumption and age of information (AoI) are critical in real-time industrial applications. Existing works do not optimize energy consumption and AoI simultaneously for data compression and transmission in intelligent reflecting surface (IRS) aided industrial systems. In this paper, we investigate efficient data compression and transmission strategy in IRS-aided industrial systems. The objective is to minimize the energy consumption while ensuring the timeliness of the data with the consideration of AoI, by adjusting the data compression ratio and phase shift dynamically. Specifically, we formulate a long-term stochastic optimization problem and further adopt Lyapunov optimization to decouple the original optimization problem into a set of one-slot optimization problems. Following that, we propose an online algorithm called Lagrange-assisted Snow Ablation Optimizer (LSAO) to transform complex constraints and solve the one-slot optimization problem. We conduct simulations to verify the effectiveness of the proposed algorithm. Experimental results show that our proposed LSAO outperforms alternative algorithms in optimizing the time-average energy consumption and AoI.
Keywords:
Age of information
edge computing
intelligent reflecting surface
Lyapunov optimization
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