arXiv subjects

Marc Sanchez Net

Publications and source records attributed to Marc Sanchez Net.

8 recordsLinked to original sources

arXiv2026

Sub Terahertz LEO Satellite Communication: Vision, Opportunities, and Challenges toward the First Prototype in Space

The landscape of sub-terahertz (sub-THz, 100GHz - 300GHz) wireless technology evolved drastically over the last two decades - from only a few niche use cases in sensing and ultra-short-range communications in early 2000s toward operational multi-kilometer range 100GBbit/s+ wireless backhaul links demonstrated recently. Building on this momentum, this article explores the feasibility of extending sub-THz communications to 100-km-scale satellite links. We first assess the technological readiness of emerging sub-THz hardware and signal-processing techniques, highlighting their potential to support long-range operation in low-Earth-orbit (LEO) systems. We then outline the unique role that sub-THz links can play as a complementary solution to existing millimeter-wave and optical (``laser'') satellite technologies, offering additional capacity, improved resilience, and new architectural flexibility. We further discuss open research and engineering challenges toward implementing such sub-THz satellite communication systems in practice. We finally outline the key state-of-the-art solutions and the roadmap of TeraLink, an ongoing international R&D project aiming to build and launch, through an approved NASA CSLI space mission, the first hardware prototype of sub-THz LEO satellite communications in space.

cs.NI↗

arXiv2026

Toward the Internet of Space Things: Performance Analysis of LEO Satellite Relay Networks using mmWave and sub-THz links

As the commercial space economy expands, existing ground-based infrastructure faces severe bottlenecks in supporting the data-intensive continuous connectivity needs of next-generation "space users," including CubeSats, space data centers, and more. Even when utilizing existing Ku-band ground relay networks, the contact time with a CubeSat at low-Earth orbit (LEO) is often still limited to minutes per day only. This paper analyzes an alternative system design that leverages emerging high-rate millimeter-wave (mmWave) and sub-terahertz (sub-THz) inter-satellite links to build a high-throughput and high-availability satellite-based relay backbone for space vehicles. To evaluate this concept, we develop a comprehensive mathematical framework that jointly incorporates complex time-variant orbital dynamics and mmWave/sub-THz link characteristics. We then derive the key performance indicators, including contact probability, channel capacity, and energy efficiency. The numerical results, cross-verified by computer simulations, demonstrate that such systems can provide improvements of up to several orders of magnitude compared to existing networks of ground stations. Notably, we identify a fundamental bound on download capacity and show that continuous 24/7 connectivity becomes achievable with only ten LEO relay satellites. These findings establish mmWave and sub-THz satellite relay networks as a promising, scalable, and energy-efficient solution, thus unlocking improved connectivity with various space vehicles of tomorrow.

cs.NI↗

arXiv2025

Learning Decentralized Routing Policies via Graph Attention-based Multi-Agent Reinforcement Learning in Lunar Delay-Tolerant Networks

We present a fully decentralized routing framework for multi-robot exploration missions operating under the constraints of a Lunar Delay-Tolerant Network (LDTN). In this setting, autonomous rovers must relay collected data to a lander under intermittent connectivity and unknown mobility patterns. We formulate the problem as a Partially Observable Markov Decision Problem (POMDP) and propose a Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL) policy that performs Centralized Training, Decentralized Execution (CTDE). Our method relies only on local observations and does not require global topology updates or packet replication, unlike classical approaches such as shortest path and controlled flooding-based algorithms. Through Monte Carlo simulations in randomized exploration environments, GAT-MARL provides higher delivery rates, no duplications, and fewer packet losses, and is able to leverage short-term mobility forecasts; offering a scalable solution for future space robotic systems for planetary exploration, as demonstrated by successful generalization to larger rover teams.

stat.ML↗

arXiv2025

Agile, Autonomous Spacecraft Constellations with Disruption Tolerant Networking to Monitor Precipitation and Urban Floods

Fully re-orientable small spacecraft are now supported by commercial technologies, allowing them to point their instruments in any direction and capture images, with short notice. When combined with improved onboard processing, and implemented on a constellation of inter-communicable satellites, this intelligent agility can significantly increase responsiveness to transient or evolving phenomena. We demonstrate a ground-based and onboard algorithmic framework that combines orbital mechanics, attitude control, inter-satellite communication, intelligent prediction and planning to schedule the time-varying, re-orientation of agile, small satellites in a constellation. Planner intelligence is improved by updating the predictive value of future space-time observations based on shared observations of evolving episodic precipitation and urban flood forecasts. Reliable inter-satellite communication within a fast, dynamic constellation topology is modeled in the physical, access control and network layer. We apply the framework on a representative 24-satellite constellation observing 5 global regions. Results show appropriately low latency in information exchange (average within 1/3rd available time for implicit consensus), enabling the onboard scheduler to observe ~7% more flood magnitude than a ground-based implementation. Both onboard and offline versions performed ~98% better than constellations without agility.

cs.RO↗

arXiv2022

PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication. Accurate radio signal strength prediction enables communication-aware exploration. Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-restricted (e.g. subterranean) environments. In this work, we present Propagation Environment Modeling and Learning (PropEM-L), a framework which leverages real-time sensor-derived 3D geometric representations of an environment to extract information about line of sight between radios and attenuating walls/obstacles in order to accurately predict received signal strength (RSS). Our data-driven approach combines the strengths of well-known models of signal propagation phenomena (e.g. shadowing, reflection, diffraction) and machine learning, and can adapt online to new environments. We demonstrate the performance of PropEM-L on a six-robot team in a communication-restricted environment with subway-like, mine-like, and cave-like characteristics, constructed for the 2021 DARPA Subterranean Challenge. Our findings indicate that PropEM-L can improve signal strength prediction accuracy by up to 44% over a log-distance path loss model.

cs.RO↗

arXiv2021

Multi-Robot On-site Shared Analytics Information and Computing

Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or internet may be nonexistent. In this paper we introduce a communication-aware, computation task scheduling problem for multi-robot systems and propose an integer linear program (ILP) that optimizes the allocation of computational tasks across a network of heterogeneous robots, accounting for the networked robots' computational capabilities and for available (and possibly time-varying) communication links. We consider scheduling of a set of inter-dependent required and optional tasks modeled by a dependency graph. We present a consensus-backed scheduling architecture for shared-world, distributed systems. We validate the ILP formulation and the distributed implementation in different computation platforms and in simulated scenarios with a bias towards lunar or planetary exploration scenarios. Our results show that the proposed implementation can optimize schedules to allow a threefold increase the amount of rewarding tasks performed (e.g., science measurements) compared to an analogous system with no computational load-sharing.

cs.RO↗

arXiv2020

Autonomous Scheduling of Agile Spacecraft Constellations with Delay Tolerant Networking for Reactive Imaging

Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice. When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage. In prior work, we have demonstrated an algorithmic framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile, small spacecraft in a constellation. The proposed schedule optimization would run at the ground station autonomously, and the resultant schedules uplinked to the spacecraft for execution. The algorithm is generalizable over small steerable spacecraft, control capability, sensor specs, imaging requirements, and regions of interest. In this article, we modify the algorithm to run onboard small spacecraft, such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground control. We have developed a communication module based on Delay/Disruption Tolerant Networking (DTN) for onboard data management and routing among the satellites, which will work in conjunction with the other modules to optimize the schedule of agile communication and steering. We then apply this preliminary framework on representative constellations to simulate targeted measurements of episodic precipitation events and subsequent urban floods. The command and control efficiency of our agile algorithm is compared to non-agile (11.3x improvement) and non-DTN (21% improvement) constellations.

eess.SY↗

arXiv2020

The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks

We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous robotic agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor's requests and, if needed, transparently routes them to other robots for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, latency requirements, and multi-hop, periodic, bandwidth-limited network communications; the algorithm can minimize overall energy usage or maximize the reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license.

cs.RO↗