In large-scale Internet of things networks, efficient medium access control (MAC) is critical due to the growing number of devices competing for limited communication resources. In this work, we consider a new challenge in which a set of nodes must transmit a set of shared messages to a central controller, without inter-node communication or retransmissions. Messages are distributed among random subsets of nodes, which must implicitly coordinate their transmissions over shared communication opportunities. The objective is to guarantee the delivery of all shared messages, regardless of which nodes transmit them. We first prove the optimality of deterministic strategies, and characterize the success rate degradation of a deterministic strategy under dynamic message-transmission patterns. To solve this problem, we propose a decentralized learning-based framework that enables nodes to autonomously synthesize deterministic transmission strategies aiming to maximize message delivery success, together with an online adaptation mechanism that maintains stable performance in dynamic scenarios. Extensive simulations validate the framework's effectiveness, scalability, and adaptability, demonstrating its robustness to varying network sizes and fast adaptation to dynamic changes in transmission patterns, outperforming existing multi-armed bandit approaches.

翻译：在大规模物联网网络中，由于竞争有限通信资源的设备数量不断增长，高效的介质访问控制（MAC）至关重要。本文研究了一个新挑战：一组节点必须将一组共享消息传输至中央控制器，且节点间无通信或重传机制。消息随机分布于节点的子集中，这些节点必须在共享通信机会上隐式协调其传输。目标是确保所有共享消息的可靠交付，无论由哪些节点进行传输。我们首先证明了确定性策略的最优性，并刻画了动态消息传输模式下确定性策略成功率下降的特性。为解决该问题，我们提出了一种基于去中心化学习的框架，使节点能够自主合成确定性传输策略以最大化消息交付成功率，同时配备在线自适应机制以在动态场景中保持稳定性能。大量仿真验证了该框架的有效性、可扩展性与适应性，表明其对不同网络规模的鲁棒性以及对传输模式动态变化的快速适应能力，其性能优于现有的多臂赌博机方法。