In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately $67\%$ in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately $30\%$. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately $50\%$ w.r.t. the baseline.

中文翻译：

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学习的多跳设备间通信联合能量谱效率优化

在某些情况下，例如关键的公共安全通信网络，现场可用（OSA）用户设备（UE）可能仅部分连接到网络基础架构，例如由于物理损坏或当局的故意停用。在这项工作中，我们考虑了OSA UE以无缝方式彼此连接的混合基础结构中的多跳设备到设备（D2D）通信，以便将关键信息传播到部署的命令中心。我们要解决的挑战是，在考虑OSA UE的异构特性的同时，尽可能长地保持OSA UE存活，并尽可能快地将关键信息发送到最终目的地（例如，命令中心）。我们提出了一种基于机器学习的动态适应方法，以提高联合能谱效率（ESE）。我们在学习者代理（分布式OSA UE）和调度器代理（远程无线电头或RRH）中以混合方式（部分分布式和集中式）应用Q学习方案，为此提出了下一跳选择和RRH选择算法。我们的仿真结果表明，所提出的动态自适应方法在性能上优于基线系统$67％$ 就联合能谱效率而言，其中OSA UE的能量效率受益于约 $30％$。最后，结果还表明，我们提出的带有C-RAN的框架将延迟降低了大约$50％$ 基线。