Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

中文翻译：

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超密集物联网的绿色无赠电力分配：平均场视角


无授权访问是指每个物联网 (IoT) 设备通过随机选择的资源传送其数据包，而无需花费时间进行握手过程，这是支持物联网系统所需的大规模连接的一种有前途的解决方案。在本文中，我们探索具有多数据包接收功能的无授权访问，重点是具有小数据量、零星活动和能源使用限制的超低端物联网应用。我们提出了一种功率分配方案，旨在通过考虑物联网设备的流量和能源限制来最大化吞吐量，同时最小化功耗。我们的方法采用随机几何框架和平均场博弈论来建模和分析有源物联网设备之间的相互干扰。此外，我们利用马尔可夫链模型来捕获和跟踪物联网设备的队列长度，从而能够推导稳态下的传输成功概率。仿真结果说明了最佳功率分配策略，并根据数据包传输成功概率和平均延迟评估了所提出方法的性能。