Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

This paper provides a comprehensive survey on the application and development of Artificial Intelligence (AI) and Machine Learning (ML) in satellite communication (SATCOM). It explores the increasing integration of AI/ML technologies in SATCOM systems, highlighting their potential to enhance performance, efficiency, and adaptability in response to growing demands for connectivity and data processing. The survey categorizes various use cases across different layers of satellite networks, detailing conventional solutions and the advantages of employing AI/ML techniques. It discusses the challenges associated with onboard processing, including hardware constraints, radiation tolerance, and the need for efficient resource management. Furthermore, the document examines the role of neuromorphic computing and COTS (Commercial Off-The-Shelf) devices in facilitating AI applications in space environments. Finally, we discuss the long-term developments of AI in the SATCOM sector and potential research directions. Overall, the survey emphasizes the transformative impact of AI/ML on the future of SATCOM, paving the way for innovative solutions in next-generation satellite networks.

A new constellation is designed for the multi-user non-coherent Large-Scale single input multiple output (LS-SIMO) uplink system based on M-DPSK, which is combined with a Bit Interleaved Coded Modulation-Iterative Decoding (BICM-ID) scheme for attaining improved performance. We conceive a new approach for constructing EXIT charts parameterized by the number of antennas used for optimizing the design. Finally, the system performance is analyzed and compared to previous work. This evaluation shows an overall improvement of two orders of magnitude demonstrating a dramatic reduction in the required number of antennas.

Onboard satellite communication systems generate and manage coverage beams over the Earth. Depending on data traffic requirements, the number of beams, side lobe levels, nulls, and EIRP, their beamwidth must be efficiently generated and managed. Therefore, this paper describes an approach for beam pattern synthesis applied to geostationary satellite communication systems. The beam pattern synthesis can generate beams with a beamwidth variation from 0.45° to 1.5°, which can be controlled independently for the two principal cuts. In addition, other requirements have been considered, e.g., latitude, and longitude, required EIRP, minimum and maximum side love levels for the two principal cuts, and nulling direction. The output of the synthesizer is a weight matrix with beamforming coefficients of the required beam. The direct radiating array in this contribution utilizes an open-ended waveguide antenna as unit cell elements with a period of 0.875λ ₀ designed to work in left-hand circular polarization in the frequency band from 17.7 to 20.1 GHz. Since this design is intended for high-data rates applications, the minimum beamwidth requirements are very narrow. Therefore, 36 × 36 sub-arrays of 4 × 4 unit cells with a period of 3.5λ are considered to accomplish the beamwidth requirements while maintaining reduced computational and time resources for the weight matrix calculation compared to the conventional counterpart of 144×144 unit cells. The results show that the algorithm, which uses the surrogate optimizer, can compute the weight matrix and synthesize the beam with a slight deviation from the input data.

Maritime transport has become important due to its ability to internationally unite all continents. In turn, during the last two years, we have observed that the increase of consumer goods has resulted in global shipping deadlocks. In addition, the future goes through the role of ports and efficiency in maritime transport to decarbonize its impact on the environment. In order to improve the economy and people’s lives, in this work, we propose to enhance services offered in maritime logistics. To do this, a communications system is designed on the deck of ships to transmit data through a constellation of satellites using interconnected smart devices based on IoT. Among the services, we highlight the monitoring and tracking of refrigerated containers, the transmission of geolocation data from Global Positioning System (GPS), and security through the Automatic Identification System (AIS). This information will be used for a fleet of ships to make better decisions and help guarantee the status of the cargo and maritime safety on the routes. The system design, network dimensioning, and a communications protocol for decision-making will be presented.