基于分层异构图神经网络的灾后供电通信智能网络中的信息年龄-能量-频谱优化
AoI-Energy-Spectrum Optimization in Post-Disaster Powered Communication Intelligent Network via Hierarchical Heterogeneous Graph Neural Network
AI总结：
本文提出灾后供电通信智能网络PDPCIN及分层异构图神经网络HHGNN框架，联合优化信息年龄、能效和频谱效率，有效应对灾后通信中断与NP-hard多维资源调度挑战。
摘要：
本文设计了灾后供电通信智能网络（PDPCIN），以解决灾区内地面基站（GBS）故障导致的通信中断问题。PDPCIN采用无人机（UAV）为受灾区域提供无线数据采集（WDC）和无线能量传输（WET），并利用低轨卫星（LEO SATs）将UAV数据中继至最近的存活GBS。为确保灾后基本通信的同时联合优化信息年龄（AoI）、能效和频谱效率，提出了智能同步无人机（IS-UAV）架构、基于AoI的四阈值更新（AFTU）机制和动态多LEO接入（DMLA）策略。然而仍存在三个关键挑战：时变的任务-资源不平衡、多设备调度导致的复杂拓扑，以及多维度量优化中的非线性耦合，使得系统优化成为NP-hard问题。因此，本文提出了分层异构图神经网络（HHGNN）框架。该框架将异构设备节点及其通信关系建模为分层异构图结构，集成了我们定义的图感知、交换和掩码层来处理网络的输入、特征传播和输出。为搜索合适数量的单LEO SAT，我们提出了单LEO SAT密度优化（S-LSDO）算法。最后，我们将所提方案与最先进的基准进行比较，验证了其在AoI、能效和频谱效率协同优化方面的优越性。基于此，我们推导了AoI和停滞AoI比例的期望值表达式。
英文摘要：
This paper designs a post-disaster powered communication intelligent network (PDPCIN) to address communication disruptions caused by ground base station (GBS) failures within the post-disaster area. PDPCIN employs unmanned aerial vehicles (UAVs) to provide wireless data collection (WDC) and wireless energy transmission (WET) for affected areas and leverages low earth orbit satellites (LEO SATs) to relay UAV data to the nearest survival GBS. To ensure basic post-disaster communication while co-optimizing age of information (AoI), energy efficiency, and spectrum efficiency, intelligent synchronization-UAV (IS-UAV) architecture, AoI-based four thresholds updating (AFTU) mechanism, and Dynamic multi-LEO access (DMLA) strategy are proposed. However, three key challenges remain: time-varying task-resource imbalances, complex topology caused by multi-device scheduling, and nonlinear coupling in multidimensional metric optimization, making system optimization NP-hard. Therefore, this paper proposes a hierarchical heterogeneous graph neural networks (HHGNN) framework. It models heterogeneous device nodes and their communication relations as a hierarchical heterogeneous graph structure, integrating our defined graph sensing, exchange, and mask layer to handle the network's input, feature propagation, and output. To search appropriate number of single-LEO SATs, we propose single-LEO SAT density optimization (S-LSDO) algorithm. Finally, we compare the proposed scheme with state-of-the-art benchmarks to validate its superior collaborative optimization of AoI, energy efficiency, and spectrum efficiency. Based on this, we derive the expressions for the expected values of AoI and stagnant AoI proportion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
