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<p>A crucial component of communication systems’ architecture, communication scheduling is essential to guaranteeing its effectiveness, dependability, and smooth operation. Fundamentally, communication scheduling controls the prioritization, frequency, and timing of information exchanges between various actors in these systems. Scheduling eliminates conflicts in shared communication channels, avoids network congestion, and maximizes resource efficiency by carefully planning when and how data is transferred. In situations like wireless networks, when bandwidth is scarce and shared among several devices, this careful management becomes more important to prevent interference. Additionally, scheduling guarantees timely delivery and reduces latency in real-time applications and systems handling vital information. Effective scheduling saves energy and extends network life in resource-constrained contexts like sensor networks or Internet of Things (IoT) devices. The foundation of efficient, dependable, and responsive communication systems is essentially efficient communication scheduling, which permits smooth data flow while dynamically adjusting to a variety of changing network conditions. For this reason, this work introduces a unique Communication Scheduling utilizing Multi-Agent Reinforcement Learning (CS-MARL) approach that combines distributed execution with centralized learning. Additionally, by offering a scalable and flexible solution for communication scheduling inside complex systems, the suggested strategy incorporates a notion known as "communication lagging" to create a balance between centralized learning and decentralized decision-making. Additionally, the effectiveness of the CS-MARL technique in communication scheduling in complicated situations is assessed. With execution times of 7.375&#xa0;s, 5.910&#xa0;s, and 4.610&#xa0;s for agent3, agent4, and agent5, respectively, the CS-MARL algorithm consistently outperforms all others, yielding the biggest improvement.</p>

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Communication scheduling via proposed multi-agent reinforcement learning with centralized learning and distributed execution

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Ajay Nagendra Nama

摘要

A crucial component of communication systems’ architecture, communication scheduling is essential to guaranteeing its effectiveness, dependability, and smooth operation. Fundamentally, communication scheduling controls the prioritization, frequency, and timing of information exchanges between various actors in these systems. Scheduling eliminates conflicts in shared communication channels, avoids network congestion, and maximizes resource efficiency by carefully planning when and how data is transferred. In situations like wireless networks, when bandwidth is scarce and shared among several devices, this careful management becomes more important to prevent interference. Additionally, scheduling guarantees timely delivery and reduces latency in real-time applications and systems handling vital information. Effective scheduling saves energy and extends network life in resource-constrained contexts like sensor networks or Internet of Things (IoT) devices. The foundation of efficient, dependable, and responsive communication systems is essentially efficient communication scheduling, which permits smooth data flow while dynamically adjusting to a variety of changing network conditions. For this reason, this work introduces a unique Communication Scheduling utilizing Multi-Agent Reinforcement Learning (CS-MARL) approach that combines distributed execution with centralized learning. Additionally, by offering a scalable and flexible solution for communication scheduling inside complex systems, the suggested strategy incorporates a notion known as "communication lagging" to create a balance between centralized learning and decentralized decision-making. Additionally, the effectiveness of the CS-MARL technique in communication scheduling in complicated situations is assessed. With execution times of 7.375 s, 5.910 s, and 4.610 s for agent3, agent4, and agent5, respectively, the CS-MARL algorithm consistently outperforms all others, yielding the biggest improvement.