# Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing

arXiv: https://arxiv.org/abs/2308.11152 · HTML: https://mlsanity.com/paper/2308.11152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence](https://mlsanity.com/paper/1910.09594/) (2019)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Neuromorphic Integrated Sensing and Communications](https://mlsanity.com/paper/2209.11891/) (2022)
- [End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence](https://mlsanity.com/paper/2009.01527/) (2020)
- [Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns](https://mlsanity.com/paper/2010.14217/) (2020)
- [Spiking Neural Networks -- Part III: Neuromorphic Communications](https://mlsanity.com/paper/2010.14220/) (2020)
- [Traffic Simulator for Multibeam Satellite Communication Systems](https://mlsanity.com/paper/2007.07148/) (2020)
- [On the Performance of Non-Terrestrial Networks to Support the Internet of Things](https://mlsanity.com/paper/2212.01614/) (2022)

## Built on by (most cited first, 2 of 2)

- [Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models](https://mlsanity.com/paper/2409.03901/) (2024)
- [Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats](https://mlsanity.com/paper/2504.03891/) (2025)

## Abstract (arXiv)

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× 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.