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Learning-Based Deterministic Delay Performance Guarantee Strategy in RIS-Assisted Communication Networks
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DOI：10.1109/JIOT.2024.3465570.png)
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摘要
En 中文
In order to satisfy the requirements for service transformation and upgrading toward industrial digitization, networking, and intelligence, sixth generation-enabled industrial Internet of Things (IIoT) imposes new requirements on deterministic delay. However, the existing best-effort communication networks increase the uncertainty of transmission, making it difficult for users to ensure deterministic delay performance. In this article, we propose a deterministic delay guarantee strategy (DDGS) under reconfigurable intelligent surface (RIS)-assisted communication networks to ensure network performance in IIoT scenarios. In particular, we utilize stochastic network calculus (SNCs) to derive the probability that the delay falls within a specific time window, characterizing the probabilistic bounds of deterministic delay. Then, we explore the relationship between delay determinacy and wireless resources by jointly optimizing the transmit power, the channel blocklength allocation, and the phase-shift matrix at the RIS to maximize delay determinacy. Based on the interdependence of action choices among users and past experience, this article proposes a performance guarantee parameterized deep Q-network (PG-PDQN) algorithm to solve the complex problem containing a mixture of discrete and continuous action spaces. Simulation results show that the DDGS strategy significantly improves the delay determinacy compared to other strategies, and the PG-PDQN algorithm has good convergence, thus effectively improving the network performance.
Keyword:
Delays
Industrial Internet of Things
Communication networks
Resource management
Jitter
6G mobile communication
Reliability
Deep reinforcement learning (DRL)
deterministic delay
industrial Internet of Things (IIoT)
performance guarantee strategy
reconfigurable intelligence surface
stochastic network calculus (SNCs)
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
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