The latest satellite communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic. As pure optimization-based solutions have shown to be computationally tedious and to lack flexibility, machine learning (ML)-based methods have emerged as promising alternatives. We investigate the application of energy-efficient brain-inspired ML models for on-board radio resource management. Apart from software simulation, we report extensive experimental results leveraging the recently released Intel Loihi 2 chip. To benchmark the performance of the proposed model, we implement conventional convolutional neural networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. Most notably, for relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$\times$ as compared to the CNN-based reference platform. Our findings point to the significant potential of neuromorphic computing and SNNs in supporting on-board SatCom operations, paving the way for enhanced efficiency and sustainability in future SatCom systems.

翻译：最新的卫星通信任务以完全可重构的星载软件定义有效载荷为特征，能够根据系统流量的时空变化自适应调整无线电资源。由于纯优化求解方法已被证明计算繁琐且缺乏灵活性，基于机器学习的方法已成为有前景的替代方案。我们研究了用于星载无线电资源管理的节能类脑机器学习模型。除软件仿真外，我们利用近期发布的Intel Loihi 2芯片报告了大量实验结果。为评估所提出模型的性能，我们在Xilinx Versal VCK5000上实现了传统卷积神经网络，并针对不同流量需求提供了精度、查准率、查全率和能效的详细对比。最值得注意的是，在相关负载条件下，与基于CNN的参考平台相比，在Loihi 2上实现的脉冲神经网络在实现超过100倍功耗降低的同时，获得了更高精度。我们的研究结果表明，神经形态计算与SNN在支持星载卫星通信操作方面具有显著潜力，为未来卫星通信系统提升效能与可持续性奠定了基础。