This paper presents a comprehensive set of radio resource management (RRM) algorithms for large constellations of non-geostationary orbit (NGSO) satellites. These algorithms address beam placement, satellite routing, beam hopping, and frequency channel allocation, and they do so in a way that remains computationally efficient when scaling to thousands of satellites. Unlike many previous papers, this paper tests its approach using a realistic subscriber model based on actual population densities. Simulation results show that the proposed algorithms for cell layout, satellite routing, and allocation of frequency-time resources significantly outperform conventional methods, yielding higher data rates for subscribers. The paper then uses this simulation to determine the spatial distribution of user data rate across the continental US, and shows the relationship between the data rates experienced in a region and that region’s population density and subscriber density.
Realistic fifth-generation (5G) deployments must contend with time-correlated fading and power amplifier (PA) nonlinearity impairments that are often studied in isolation. This work introduces a unified MATLAB 5G-Toolbox framework and custom dataset for deep learning (DL)-based multiple-input multiple-output (MIMO) detection under power amplifier (PA) nonlinearity and channel memory. Specifically, we implement four detectors based on a fully connected neural network (FCNN), a convolutional neural network (CNN), a residual network (ResNet), and a long-short-term memory (LSTM) network to evaluate their bit error rate (BER) under quadrature amplitude modulation (QAM). A sphere decoding (SD) algorithm, which employs minimum mean squared error (MMSE) initialization and Schnorr–Euchner (SE) ordering, is used as a near maximum likelihood (ML) benchmark. Extensive Monte Carlo simulations are performed over an Eb/N0 range of 0 to 18 dB (with an effective 6 dB signal-to-noise ratio (SNR) shift for 16-QAM). Our results demonstrate that CNN- and ResNet-based detectors achieve competitive performance within 2–3 dB of ML performance under severe nonlinearity, while reducing inference run-time by more than 50% on an Intel i7 CPU compared to ML and SD algorithms. In the urban macro-scenario, where rapid temporal variations and channel memory degrade conventional models, an LSTM-based detector demonstrates superior robustness by explicitly leveraging time-series dependencies. A detailed cost–complexity analysis is also provided, reporting floating point operations (FLOPs) and the actual run-time per block on specified hardware to quantify the trade-off between detection accuracy and computational effort. .
Space debris poses a growing threat to the safety of man-made operational space-borne systems, demanding reliable detection and tracking solutions to mitigate collision risks. Due to the limited data rate resulting from non-sequential computations, practical radar systems typically exhibit low measurement accuracy, particularly over long distances in space. In this work, we propose a Kalman filter-based approach for accurate range and velocity estimation of space debris using space-based linear frequency modulated (LFM) radars. By enabling sequential processing, the proposed method enhances both range and velocity resolution while maintaining a low sampling rate requirement for analog-to-digital converters (ADCs), making it suitable for real-world deployment. The performance of the proposed method is evaluated through simulations and benchmarked against conventional approaches under low signal-to-noise ratio (SNR) conditions which demonstrate its effectiveness in improving tracking accuracy. Simulation results show that the Kalman filter significantly reduces velocity estimation error, especially under low SNR levels. This confirms its practical advantage in space-based debris monitoring applications.
Light Fidelity (LiFi) for intra-satellite communication offers immense advantages like flexible AIT and reduced harness complexity. This paper presents a simulation framework for light propagation with the primary goal to optimize positioning, spatial structures and other system parameters for intra-satellite LiFi-communication architectures. Initial tests were conducted with generic scenarios, such as obstacle simulation and the alignment of transmitters and receivers. In a subsequent step, a complete 3D model of a CubeSat was integrated into the simulation. The results demonstrate that the framework is already suitable for optimizing and visualizing optical communication structures. This allows for virtual analysis, enhancing or complementing physical test setups in the early stages of development.
LunaNet is the framework specified by NASA and key space agencies to deliver communications and positioning, navigation and timing (PNT) services in cislunar space. It is an open architecture that includes LunaNet Service Providers (LNSP) to deliver network services to users. Delay and Disruption Tolerant Networking (DTN) is at the core of LunaNet. DTN is a networking technology specifically designed for interplanetary networks, operating in environments where link delays and disruptions are common, in which traditional TCP/IP architecture does not perform properly. This paper presents the demonstration of an operational LunaNet Service Provider DTN network on the International Space Station (ISS) using the SPATIAM DTN Platform. The demonstration accomplished the setup and operation of an LNSP network for 18 days connecting payloads from the ISS with Earth ground users. This paper explains its key objectives, architecture, results, and future work.
