Link budgets are widely applied to evaluate communication links for low Earth Orbit (LEO) satellites. However, approaches to calculate the received power from LEO satellites have followed similar procedures to those for Geostationary (GEO) satellites and other fixed distance wireless systems, ignoring the satellite mobility that causes continuous changes in the path length and in the elevation angle. Link budgets found in the literature for LEO communication systems have commonly opted to characterize the best and worst-cases of the received signal; however, this common approach tells little about how often those cases occur, and little can be inferred about the expected received power and its measures of dispersion. This article introduces an innovative methodology to evaluate LEO link budgets using the long term statistics and probabilities of occurrence of the elevation angle, which is characterized in this work through a random variable. This characterization of the elevation angle through a random variable allows the calculation of the expected value of the received power, its standard deviation, quantiles, among other quantities. The received power is essential to calculate other link indicators such as the carrier-to-noise ratio (C/N), and with the proposed methodology it can be now calculated considering the path length and elevation angle variability that occur for LEO satellites.

The elevation angle $\theta$ is relevant for the Low Earth orbit (LEO) satellite communications since it is always changing its relative position with respect to fixed Earth stations (ES’s), and this affects the link length and received power, $P_R$. This article provides a new methodology to compute the probability density function (PDF) and cumulative distribution function (CDF) of the elevation angle, $\theta$, for diverse ES locations. This methodology requires as input parameters an ES latitude, $\varphi$, an orbit inclination value, i, and an orbit altitude, h. The elevation angle is characterized through a well known random variable, which facilitates the computation of the first and second-order statistics, and helps to determine the expected value and measures of dispersion of the angle $\theta$ for a particular ES location. The proposed methodology allows an easy and quick calculation of the elevation angle’s CDF, facilitating comparisons against CDF’s of more ES’s located at different latitudes, and longitudes, $\lambda$; as well as the comparisons of CDF’s of the elevation angle produced by different orbits. Extensive simulation results are summarized in a small table, which allows computation of the elevation angle’s CDF and PDF for multiple ES locations without requiring of simulations and statistical fitting. Finally, the proposed methodology is validated through an extensive error analysis that show the suitability of the obtained results to characterize the elevation angle.

The solar system is still uncommunicated and unknown for humankind. To acquire more knowledge about the solar system, we send satellites and rovers to explore those planets; however, it is costly and takes a lot of effort. Soil retains information about the environment of celestial bodies, and we can process that information to make decisions about future infrastructure settlements that could provide advantages for the interplanetary Internet. The interplanetary Internet communications must be scalable, interoperable, secure, and easy for data transmission. However, before thinking about carrying out soil analysis through surface exploration, we can see that the first step is to analyze it using sensing satellites studying the structure of their data collection orbits through intelligent vision. In this paper we propose the use of cameras mounted on sensing satellites for the soil analysis during orbit (high-resolution, infrared, spectral, optical) for general scanning of surface elements with AI postprocessing, and mass spectrometer for spectroscopy. This equipment will analyze the chemical composition of the surfaces, the magnetic field lines, and the material radiation, detect rocks and gas elements, and identify the surface characteristics, among others. In this paper, we discuss how to develop the architecture of an interplanetary Internet physical platform with space-to-ground observations and measurements. A satellite orbiting a celestial body will become a sensor node with physical layers designed with relays and a modular setup, as well as a data transport method and location estimation sensing system, as a basis for the interplanetary Internet system. The design of the interplanetary Internet must consider the information from the analysis and observation of celestial bodies’ variables and parameters, as a fundamental flow of information that must be transported through the network to be further analyzed and used.

Intelligent signal processing holds great importance for the future of resilient, adaptable communications networks. The unique qualities of deep space require an interplanetary Internet to be highly autonomous, efficient, and adaptable to varying Quality of Service (QoS). Deep learning has shown great promise in the field of signal processing for being computationally efficient, capable of handling errors from nonlinear effects (e.g., hardware impairments), and handling low signal-to-noise ratios. A recent survey by Pham, Q.V., et al. notes that none of the papers studied the improvements in classification in the high-order modulation regime. Additionally, these papers did not explore performance of their models in resource limited environments. A hierarchical interplanetary Internet that imposes a variety of constraints on its nodes offers a unique opportunity to explore realistic tradeoffs in model performance. This paper seeks to leverage the processing, storage, and data transmission capabilities of each level of the interplanetary Internet through federated learning. This will reduce data redundancy between nodes and minimize overhead transmission costs on the network. The goals of this project are the following: (i) Detail possible insights into future channel estimation techniques applied to noisy, nonlinear models. (ii) Explore application of deep learning models for high-order modulation schemes. (iii) Quantify the resource-demand reduction resulting from the use of a deep neural network for intelligent signal processing. (iv) Analyze the adaptability of an interdependent system of deep neural networks in the context of a centralized/decentralized federated learning network.

Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human settlements in the future. Observation and understanding of the land conditions of those celestial bodies is vital to learn more about those celestial bodies, to generate accurate maps of them, to look for natural resources of interest, and to evaluate the feasibility and help in the preparation of future land missions. A satellite constellation constitutes an important infrastructure element to observe those celestial bodies and to transmit the retrieved information back to Earth. Nonetheless, the operation of sensing satellites in other planets needs understanding of the requirements to perform such observations. In this paper, we discuss those sensing requirements from the point of view of orbits and payload requirements for one of our closest neighbors of the solar system (Moon, Mars). To analyze the orbit of the sensing satellite, we discuss the required altitude to facilitate ground observation, the orbit’s conditions (such as radiation levels and orbital perturbations, among others), suitable orbit configurations, required number of satellites, and ways to estimate the required time to perform full observation of the celestial body. To evaluate suitable payloads, we discuss available information in the literature (such as known atmospheric and land conditions) to determine the best observation frequencies and determine the best kind of payload (such as sensors, a camera, or a lower frequency observation payload) to study that celestial body. Finally, we discuss some important considerations such as the requirements of satellite communication link to transmit the retrieved information back to Earth.