With increasing demand for broadband services provided by satellite constellation networks, routing and traffic management have become increasingly relevant topics. To enable global coverage and end-to-end connectivity, these systems rely on inter-satellite links to span space-borne networks. To fulfill the quality of service requirements of significant network loads, the traffic load needs to be balanced optimally. As rule-based techniques to solve the underlying multi-commodity flow problem are too complex to comply with on-board processing power limitations, specifically tailored light-weight solutions are required. To this end, we propose an adaptive traffic engineering approach based on deep reinforcement learning. We explore a policy-based approach for a flexible flow allocation between candidate paths. We compare the schemes with state-of-the-art benchmarks based on heuristics, and an optimal benchmark using linear programming. The results highlight that the proposed scheme is able to approximate optimal solutions to the multi-commodity flow problem and can learn suitable policies for diverse sets of paths. Moreover, after training, the approach exhibits low complexity in inference, and is thus well-suited to be included in the controller logic of in-space distributed software defined networks.
The rapid growth in Internet of Things (IoT) applications has driven demand for ubiquitous connectivity, reaching beyond terrestrial networks to include satellite communication solutions. This paper investigates the application of Narrowband IoT (NB-IoT) within Non-Terrestrial Networks (NTNs), specifically focusing on direct-to-satellite (DtS) communications for IoT in Low Earth Orbit (LEO) constellations. This study first provides an overview of NB-IoT’s protocol adaptations required for NTN use, followed by the introduction of an analytical model for network throughput that incorporates the dependence on key system parameters, such as satellite velocity, spot size, and ground node density. Our results analyze key NB-IoT parameters affecting network performance, providing insights on configuration strategies to optimize connectivity for remote IoT deployments via LEO satellites. The findings contribute to understanding the feasibility of NB-IoT for DtS communication, highlighting practical considerations and performance trade-offs for deploying IoT over NTN.
Non-Terrestrial Networks (NTN) are poised to revolutionize 5G and 6G networks by integrating terrestrial and space-based cloud systems, enabling dynamic task allocation for optimal performance. Despite their promise, understanding the trade-offs between terrestrial and non-terrestrial edge computing architectures remains an area for improvement. This paper presents a comprehensive latency-focused trade-off analysis using a novel real-time emulation platform that accurately models terrestrial and space cloud environments. By evaluating network latency across geodesic distances from a fixed ground gateway, we delineate scenarios where terrestrial clouds excel and identify conditions under which Space Cloud architectures surpass their terrestrial counterparts. Additionally, we analyze how server placement strategies in satellite constellations impact performance, revealing the critical interplay between server distribution and latency outcomes. These findings offer actionable insights for designing and operating hybrid cloud systems, emphasizing the need for tailored architectures to maximize the potential of NTN-based edge computing.
This simulation study aims to compare the physical layer performance of the 5th Generation (5G) New Radio Non-Terrestrial Networks (NR NTN) air interface with Digital Video Broadcasting (DVB) technologies, specifically analyzing the Second Generation Satellite Extensions (S2X) standard on the Downlink (DL) and DVB- Return Channel via Satellite 2nd generation (RCS2) standard on the Uplink (UL). The study assumes a reference geosynchronous satellite telecommunication system operating in the Ka-band frequencies. The comparison analysis of the physical layers demodulation performance on the Forward (FWD) link reveals an average spectral efficiency decrease from 15 to 26% for NR PDSCH (Physical Downlink Shared Channel) in comparison to DVB-S2X. The analysis of the Return (RTN) link indicates comparable performance between NR PUSCH (Physical Uplink Shared Channel) and DVB-RCS2, with a marginal advantage for either technology based on specific study cases. The conclusions drawn from the performance comparison emphasize the potential for optimizing NR waveform configuration to improve spectral efficiency and reduce implementation losses through enhanced channel estimation and phase tracking algorithms. Moreover, this study outcome advocates for the completion of the physical layer comparison with System Level simulations of both technologies. This would make it possible to consider additional implications such as nonlinear amplification effects, control plane overhead, and scheduling efficiency.
The aim of this paper is to introduce Bundle in Bundle Encapsulation (BIBE). BIBE is an experimental extension of the Bundle Protocol (BP), the pillar at the foundation of the Delay-/Disruption- Tolerant Networking architecture, whose use is planned in next space mission to Moon, and, in longer terms, to build an Interplanetary network. Among BIBE features, the paper analyzes in detail the optional BIBE retransmission mechanism, which should offer all the advantages of the Custody Transfer option that was present in previous versions of BP, but without its problems. BIBE applications are potentially so numerous that they cannot all be examined in one paper, thus here the focus is on those that are non-security related, such as BIBE retransmission on both unidirectional and space links, transient QoS, and transient “critical” forwarding. All these cases have been exercised using the latest version of ION, the DTN suite maintained by NASA JPL, by means of an enhanced version of a pre-existing test program. The paper concludes with a detailed study of an example of BIBE traffic, based on the use of the Wireshark network analyzer, for which a specific BIBE dissector was designed by the authors.
