Quantum computing poses a looming threat to the cryptographic primitives that secure today’s satellite communications. In response, post-quantum cryptography (PQC) has emerged to protect data against future quantum-enabled adversaries. This paper provides a comprehensive analysis of lattice-based PQC methods for secure satellite communication in the post-quantum era. We examine the performance and security of leading lattice-based schemes – including encryption/key-establishment algorithms like CRYSTALS-Kyber and NTRU, and digital signature schemes like CRYSTALS-Dilithium and FALCON – in the context of satellite systems. Real-world benchmark data from peer-reviewed sources are used to evaluate computational efficiency, bandwidth overhead, memory footprint, and energy consumption of these schemes on resource-constrained satellite hardware. We compare lattice-based approaches with classical RSA/ECC and alternative post-quantum techniques (hash-based signatures, code-based encryption), highlighting the favorable trade-offs that have led to lattice-based algorithms becoming primary candidates for standardization. Security analysis is presented to explain the hardness assumptions (e.g., Learning With Errors and NTRU problems) that underpin lattice cryptography, and to assess known cryptanalytic results, resistance to quantum attacks, and remaining security margins. We also discuss practical considerations for deploying lattice-based cryptography on satellites, including integration into existing communication protocols, potential need for hardware acceleration, and strategies for cryptographic agility in long-lived space assets. The results show that lattice-based PQC can be feasibly implemented in satellites with acceptable performance impact, providing strong security against quantum threats and a viable path to future-proof the confidentiality and authenticity of satellite communications.

Efficient usage of available network resources is a crucial factor for broadband services in interconnected satellite constellations. To meet required quality of service standards under heavy network loads, it is essential to optimize traffic distribution among the inter-satellite links. To address this challenge, we propose an adaptive traffic engineering framework based on deep reinforcement learning. Our approach employs a path-based decision-making strategy, using a centralized agent to distribute incoming flow requests on a set of candidate paths. This method approximates optimal solutions to the multi-commodity flow problem with relatively low computational complexity, making it suitable for in-space network control despite on-board processing limitations. The performance of the proposed scheme is evaluated against state-of-the-art rule-based benchmarks in various scenarios. We quantify the impact on performance of different candidate-path sets and traffic patterns. Overall, the proposed solution presents a viable approach for optimizing flow distribution in satellite constellation networks, suitable for the integration into the controller logic of software-defined networks.

Direct-to-Satellite IoT (DtS-IoT) has emerged as a promising solution for extending IoT connectivity to remote regions where terrestrial infrastructure is impractical or economically unfeasible. In such scenarios, Low-Earth Orbit (LEO) satellites act as mobile in-orbit gateways, but their dynamic nature and reliance on constrained Inter-Satellite Links (ISLs) introduce significant challenges for medium access, routing, and end-to-end performance evaluation. To address these issues, we present FLoRaSat 2, an open-source, event-driven simulator based on OMNeT++. The first version of FLoRaSat focused on device-to-satellite communication, providing a unique tool to study LoRa/LoRaWAN-based DtS-IoT systems. The present work advances FLoRaSat 2 into a full end-to-end simulation framework by integrating Medium Access Control (MAC) protocols with constellation-grade routing models, including constellation creation, dynamic ISL topology control, and a sandbox for benchmarking routing algorithms such as Directed, DDRA, and DiscoRoute. Notably, it introduces K-MAC, a repetition-based MAC scheme designed to improve resilience under Doppler effects, congestion, and intermittent visibility, while expanding the satellite and device modules to support multiple MAC protocols beyond LoRaWAN. To the best of our knowledge, FLoRaSat 2 is the only open-source simulator capable of jointly evaluating access and routing layers for constellation-grade DtS-IoT networks. Through extended benchmark scenarios on Starlink- and Iridium-like constellations, we demonstrate how the integration of MAC and routing enables a more realistic and holistic assessment of network performance, positioning FLoRaSat 2 as a versatile platform for the design and optimization of next-generation satellite IoT systems.

The size and complexity of emerging low-earth orbit (LEO) satellite communications (Satcom) constellations introduce challenges for network traffic engineering. As low latency Satcom connectivity LEO constellations with inter-satellite link (ISL) technology take off, improvements in cost and performance are key to increasing market share. Delivering the best service to customers in this increasingly competitive marketplace is driving operators to consider new technologies to scale-up capacity whilst improving service performance and controlling expenditure. The vision at the heart of all mega-constellation systems is a Very Wide Area Network (VWAN) with low latency that can compete with land-based fibre systems. One area where ISL-enabled mega-constellation systems show promise is delivering lower latency and higher throughput communications over long distances with fewer satellite network gateways, which in turn drives the total cost of ownership of the system down substantially.

Free Space Optical Communication has emerged as a promising technology for high-speed and secure data transmission between ground stations on Earth and orbiting satellites. However, this communication technology suffers from signal attenuation due to atmospheric turbulence and beam alignment precision. Low Earth Orbit satellites play a pivotal role in optical communication due to their low altitude over the Earth surface, which mitigates the atmospheric precipitation effects. This paper introduces a novel data path control law for satellite optical communication exploiting Artificial Intelligence-based predictive weather forecasting and a node selection mechanism based on Reinforcement Learning. Extensive simulations on three case studies demonstrate that the proposed control technique achieves remarkable gains in terms of link availability with respect to other state-of-the-art solutions.

To meet the increased bandwidth demand from remote locations, new satellites are being launched to allow networks to operate at higher frequencies (i.e., Ka-band – 27.0GHz - 40.0 GHz and V-band 40.0GHz – 75GHz). At these frequencies high availability is challenged by rain attenuation. Consequently, satellite network operators are offering hybrid solutions where, during a rain outage, traffic is moved to a lower frequency secondary heterogeneous satellite network not affected by rain.   This paper evaluates a mobile and network centric handover and admission strategy using a bespoke simulation model of a rain affected star topology hybrid satellite network with primary network operating on Ka band and secondary satellite network operating at a lower frequency.    Results show that a network centric prioritised queue-based admission strategy provides a reduction in handover failure probability of about 5% without extra capacity being made available for the handover of traffic to the secondary network. When queue-based admission is used in combination with adding extra capacity in the secondary satellite network a reduction in handover failure probability of nearly 30% was achieved.   However, when more capacity is made available in the secondary network, the advantages of the network centric queue-based admission strategy slowly disappear with added capacity. This means that operators have a clear choice based on whether they believe that their secondary network has sufficient spare capacity, or whether in fact this is unlikely and therefore the extra cost of a queue-based admission strategies should be weighed against loss of revenue due to lost connections.