PAGE CONTENTS
Objectives
The SPAICE project aims to study, develop, and validate Artificial Intelligence (AI)-based signal processing techniques for satellite communications in scenarios and use cases where specific AI processors can provide a significant performance improvement with respect the current state-of-the-art.
The team has already identified and traded-off some prospective scenario candidates such as interference detection and mitigation, interference localization, link adaptation, forward error correction, channel estimation, flexible payloads, and adaptive beamforming; all of them targeted for regenerative satellites. In addition, the team has also investigated on the suitable ML architectures, frameworks and Commercial-Off-The-Shelf (COTS) AI chipsets that can bring greater benefits to their combined use in the abovementioned scenarios targeting onboard communications satellites. A combination of flexible resource allocation payload and adaptive beamforming was selected for the demonstration phase.
The SPAICE project has as its principal outcome the AI Satellite Telecommunications Testbed (AISTT), which will be the platform to test and demonstrate the selected AI-accelerated scenarios. The AISTT will take advantage of the experimental expertise and available facilities at the SnT―University of Luxembourg such as the CubeSat Laboratory and the Satellite Communications Laboratory.
Once the AISTT is successfully designed, implemented and the selected AI-accelerated scenarios are successfully tested and validated, the last objective of SPAICE is the evaluation the potential road-to-the-market of the testbed.
Challenges
The targeted improvement of this activity with respect to the existing state-of-the-art is enabling on-board real-time satellite signal processing techniques. Existing solutions usually exhibit inherent limitations in translating theory to practice when handling the computational complexity and/or the latency required for the outcomes specially when dealing with large search space or high degrees of freedom. This has been typically the motivation for the use of AI, since it has been shown to have a strong potential to overcome this challenge via data-driven solutions. In the SPAICE project, we evaluated what are the expected gains, both in terms of latency, complexity and performance that can be achieved with the application of AI-based techniques in each of the considered use cases.
System Architecture
The system simulator was built using MATLAB and feeds the cascaded joint resource allocation and beamforming algorithm with realistic geographical-based traffic demand using the Satellite Traffic Emulator developed by the SnT SIGCOM group.
A channel simulator, an antenna pattern simulator and a link budget simulator are used to compute the served throughput and compare it with the original geographical traffic demand.
The proposed ML algorithms are implemented using Tensorflow and trained using the system simulator data. For the inference part used in the AISTT, the initial AI accelerator trade-off has shown that the Xilinx Versal AI ACAP family is the best COTS chipset in terms of performance versus consumed power from their specifications.
Plan
The project has 2 phases with total duration of 26 months.
The WBS is composed of the following WPs:
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WP1 Scenario and Requirements Definition (Phase 1)
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WP2 ML Preparation and Generation of Data Sets (Phase 1)
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WP3 ML Setup, Training and Validation (Phase 2)
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WP4 AISTT Requirements, Architecture and Design (Phase 2)
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WP5 AISTT Test Plan and Implementation (Phase 2)
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WP6 AISTT Validation and User Manual (Phase 2)
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WP7 Roadmap and Lessons Learned (Phase 2)
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WP8 Project Management (Phase 1 & 2)
Current Status
As of December 2022, the SPAICE project completed its Phase 1 with a successful definition of the scenarios and requirements, definition of the ML algorithms and their framework, generation of the training data sets, and selection of the AI chipset family.
The SPAICE team is now working towards the training of the ML algorithms and the definition of the AISTT in the Phase 2 of the project.
