Energy-Efficient On-Board Radio Resource Management for Satellite
Communications via Neuromorphic Computing
- URL: http://arxiv.org/abs/2308.11152v1
- Date: Tue, 22 Aug 2023 03:13:57 GMT
- Title: Energy-Efficient On-Board Radio Resource Management for Satellite
Communications via Neuromorphic Computing
- Authors: Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins,
Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran and Symeon
Chatzinotas
- Abstract summary: We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management.
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.
- Score: 59.40731173370976
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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