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Modulating Retroreflectors for CubeSat Optical Inter Satellite Links: Modeling, Optimization, and Benchmarking

Makafui Avevor, Hossein Safi, Harald Haas, Iman Tavakkolnia

TL;DR

This work addresses the need for low‑power, compact inter‑satellite optical links for CubeSats by proposing a Modulating Retroreflector (MRR) architecture and developing a unified statistical channel model for an OOK‑modulated, retroreflector‑enabled OISL. It introduces a comprehensive framework that captures both stochastic and deterministic pointing losses, velocity aberration, and signal‑dependent noise, and derives BER, outage, and achievable information rate (AIR) metrics, optimized via Monte Carlo simulations under CubeSat SWaP constraints. The study benchmarks the MRR approach against NASA OCSD, DLR OSIRIS4CubeSat, and CLICK BC, showing comparable performance to OSIRIS4CubeSat for ranges under 500 km while consuming only about 2.5 W, and outperforming OCSD, thus offering a compelling low‑SWaP option for short‑range asymmetric CubeSat links. The results highlight the practical viability of MRRs for lightweight CubeSats, with velocity aberration and aperture sizing identified as key factors for extending range, and suggest paths for future experimental validation and integration with hybrid architectures.

Abstract

Modulating retroreflectors (MRRs) offer a promising pathway to low-complexity and energy efficient asymmetric optical inter-satellite link (OISL) for small spacecrafts, such as CubeSats. In this paper, we develop a unified statistical channel model for an on off keying modulated, retroreflector-enabled OISL. The model captures both stochastic and deterministic pointing losses, as well as signal-dependent noise. Stochastic channel distributions are approximated via Monte Carlo simulation, and system optimization is carried out under CubeSat constraints using the achievable information rate as the primary metric. In addition, we derive bit-error ratio and outage probability to evaluate communication reliability. The proposed architecture is benchmarked against three state-of-the-art CubeSat laser terminals, i.e., NASA's Optical Communications and Sensors Demonstration (OCSD), DLR's OSIRIS4CubeSat, and NASA's CLICK BC. Results indicate that an optimized MRR-based transmitter can outperform OCSD and achieve performance comparable to OSIRIS4CubeSat at ranges below 500 km, while consuming only 2.5 W of power during transmission, significantly less than conventional CubeSat optical terminals.

Modulating Retroreflectors for CubeSat Optical Inter Satellite Links: Modeling, Optimization, and Benchmarking

TL;DR

This work addresses the need for low‑power, compact inter‑satellite optical links for CubeSats by proposing a Modulating Retroreflector (MRR) architecture and developing a unified statistical channel model for an OOK‑modulated, retroreflector‑enabled OISL. It introduces a comprehensive framework that captures both stochastic and deterministic pointing losses, velocity aberration, and signal‑dependent noise, and derives BER, outage, and achievable information rate (AIR) metrics, optimized via Monte Carlo simulations under CubeSat SWaP constraints. The study benchmarks the MRR approach against NASA OCSD, DLR OSIRIS4CubeSat, and CLICK BC, showing comparable performance to OSIRIS4CubeSat for ranges under 500 km while consuming only about 2.5 W, and outperforming OCSD, thus offering a compelling low‑SWaP option for short‑range asymmetric CubeSat links. The results highlight the practical viability of MRRs for lightweight CubeSats, with velocity aberration and aperture sizing identified as key factors for extending range, and suggest paths for future experimental validation and integration with hybrid architectures.

Abstract

Modulating retroreflectors (MRRs) offer a promising pathway to low-complexity and energy efficient asymmetric optical inter-satellite link (OISL) for small spacecrafts, such as CubeSats. In this paper, we develop a unified statistical channel model for an on off keying modulated, retroreflector-enabled OISL. The model captures both stochastic and deterministic pointing losses, as well as signal-dependent noise. Stochastic channel distributions are approximated via Monte Carlo simulation, and system optimization is carried out under CubeSat constraints using the achievable information rate as the primary metric. In addition, we derive bit-error ratio and outage probability to evaluate communication reliability. The proposed architecture is benchmarked against three state-of-the-art CubeSat laser terminals, i.e., NASA's Optical Communications and Sensors Demonstration (OCSD), DLR's OSIRIS4CubeSat, and NASA's CLICK BC. Results indicate that an optimized MRR-based transmitter can outperform OCSD and achieve performance comparable to OSIRIS4CubeSat at ranges below 500 km, while consuming only 2.5 W of power during transmission, significantly less than conventional CubeSat optical terminals.
Paper Structure (34 sections, 59 equations, 20 figures, 5 tables)

This paper contains 34 sections, 59 equations, 20 figures, 5 tables.

Figures (20)

  • Figure 1: System model illustrating the round-trip communication link setup between two satellites and key parameters influencing the channel characteristics and overall system performance.
  • Figure 2: FFIP for uniform ($L_{\text{u}}$) and Gaussian ($L_{\text{g}}$) beams plotted as functions of the off-boresight angle $\theta$ using the dimensionless angular coordinate $X = ka\sin\theta$. For the parameters in Table \ref{['tab:Example Parameters']}, uniform illumination yields a divergence of $\sim$10 $\mu$rad with unit peak gain, while Gaussian illumination produces a broader divergence of $\sim$12.5 $\mu$rad and a reduced on-axis peak of $\sim$0.8.
  • Figure 3: Receiver gain profiles for varying $\theta_{\text{FOV}}/\theta_{\text{res}}$, normalized to the uniform-aperture on-axis gain.
  • Figure 4: Distribution of channel gain $f_{G_1}(g_1)$ vs. ratio of transmit divergence angle to pointing error. Larger ratios concentrate probability near the on-axis maximum.
  • Figure 5: Distribution of channel gain $f_{G_1}(g_1)$ vs. receiver FOV. Wider FOVs increase gain.
  • ...and 15 more figures