Supplemental Coverage from Space (SCS)

• Supplemental Coverage from Space (SCS) is a multidisciplinary approach that integrates space-based assets with terrestrial systems to enhance wireless coverage and imaging fidelity.
• SCS in DS2D communications uses dual carrier strategies with PUL and SUL bands, enabling extended uplink coverage even at low elevation and beam-edge regions.
• In space VLBI, SCS optimizes orbital configurations to improve (u,v)-coverage, achieving high-fidelity astronomical imaging through inverse design and simulation techniques.

Supplemental Coverage from Space (SCS) refers to the augmentation of terrestrial systems with capabilities enabled by space-based assets, addressing coverage, robustness, and performance gaps unattainable by ground infrastructure alone. In telecommunications, SCS primarily encompasses methods for extending wireless service coverage, enhancing uplink reliability for direct satellite-to-device (DS2D) communication through techniques such as supplemental uplink (SUL) bands. In radio astronomy and interferometry, SCS strategies include the deployment of spaceborne radio telescopes or interferometer elements to optimize (u,v)-coverage, improving imaging fidelity and filling gaps inaccessible to ground-based arrays. SCS frameworks are inherently multidisciplinary, integrating link-margin analysis, orbital mechanics, frequency selection, and resource-constrained terminal operation.

1. Fundamental Architectures of Supplemental Coverage from Space

SCS in DS2D communication leverages non-terrestrial networks (NTNs), specifically using low Earth orbit (LEO) satellites as regenerative nodes that form multiple high-gain beams covering portions of the terrestrial surface. Standard handheld user equipment (UE) communicate directly with these satellites via two distinct frequency domains: a high-frequency primary uplink (PUL) carrier (typically Ka-band, $f_p \approx 30$ GHz) and a lower-frequency SUL carrier (typically L-band, $f_s \approx 1.6$ GHz) (Shrestha et al., 23 Feb 2026). The architecture operates in an either-or uplink modality: the UE transmits on a single selected carrier at any time, conforming to strict power limits ($P_t \leq 23$ dBm) and front-end simplicity.

In the context of space VLBI (very long baseline interferometry), SCS introduces space telescopes into baseline arrays with ground stations, substantially improving (u,v)-plane coverage. Optimization of satellite orbits is performed to match desired (u,v)-coverage maps, facilitating imaging performance on par with or exceeding pure ground-based systems (Bulygin et al., 2024).

2. Link-Margin and Elevation Modeling for DS2D SCS

Link margin analysis underpins the SCS design in DS2D. The slant range from UE to satellite as a function of elevation angle $\theta$ is given by

$$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$

where $R_E=6371$ km and $h \approx 600$ km is the LEO altitude. The free-space path-loss (FSPL) at frequency $f$ (in MHz), distance $d$ (in km) is

$$PL_{FS}(f,d) = 32.45 + 20\log_{10}(f) + 20\log_{10}(d)$$

Received satellite power is modeled as

$f_s \approx 1.6$0

where $f_s \approx 1.6$1 models beam-edge attenuation, $f_s \approx 1.6$2 is elevation-dependent atmospheric loss (increasing at low $f_s \approx 1.6$3 and high $f_s \approx 1.6$4 per ITU-R models), and $f_s \approx 1.6$5 is implementation loss (Shrestha et al., 23 Feb 2026). The uplink SNR is

$f_s \approx 1.6$6

with $f_s \approx 1.6$7 dBm/Hz $f_s \approx 1.6$8. The predicted link margin is

$f_s \approx 1.6$9

SUL operation extends uplink coverage into low-elevation and beam-edge regions where Ka-band PUL is impaired by path loss and high $P_t \leq 23$0.

3. Elevation-Aware SUL Activation and Carrier Selection

Intelligent carrier selection is implemented via an onboard algorithm that leverages ephemeris-derived $P_t \leq 23$1 to predict $P_t \leq 23$2 (PUL) and $P_t \leq 23$3 (SUL). The algorithm uses two threshold margins: a safety margin $P_t \leq 23$4 to absorb modeling uncertainties, and a hysteresis margin $P_t \leq 23$5 to minimize ping-pong switching. The logic is

• If on PUL and $P_t \leq 23$6 and $P_t \leq 23$7, switch to SUL. Otherwise, remain on PUL.
• If on SUL and $P_t \leq 23$8, switch to PUL. Otherwise, remain on SUL.

Carrier switching occurs only when the intended new carrier differs from the current. Predictable LEO geometry allows deterministic scheduling of transitions, with typical operation yielding a single transition per satellite pass for practical hysteresis margins ($P_t \leq 23$9 dB) (Shrestha et al., 23 Feb 2026).

4. Performance Metrics and Simulation Results in DS2D SCS

SCS performance is systematically characterized by:

• $\theta$0: minimum elevation providing target SNR
• $\theta$1: extension in $\theta$2, i.e., coverage gain from SUL activation
• $\theta$3: uplink availability, defined as the fraction or cumulative distribution (CDF) of the pass span with SNR exceeding threshold
• $\theta$4: number of uplink carrier transitions per pass

Table of Simulation Parameters (DS2D SCS):

Parameter	PUL	SUL
Frequency ($\theta$5)	30 GHz (Ka)	1.6 GHz (L)
Sat. gain ($\theta$6)	65 dBi	45 dBi
$\theta$7	500 K	290 K
$\theta$8	$\theta$9 dB	$$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$0 dB
$$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$1	23 dBm	23 dBm
BW ($$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$2)	10 MHz	10 MHz

Key findings indicate that enabling SUL operation reduces $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$3 from 30–40° (PUL-only) to $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$410°, extending effective uplink coverage by over 20° in elevation (hundreds of km at the surface). Uplink availability becomes nearly uniform across $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$5 with SUL enabled, and SUL activation induces minimal carrier transitions per pass (Shrestha et al., 23 Feb 2026).

5. SCS in Space VLBI: Optimization of Orbital Configuration for (u,v)-Coverage

In space radio interferometry, SCS objectives shift toward maximizing synthesized aperture quality via optimal (u,v)-coverage. Each baseline between ground or space telescopes is projected onto the source sky via

$$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$6

where $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$7 are baseline Cartesian components, and $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$8 are source coordinates. The supplemental coverage problem is posed as an inverse design: optimizing satellite orbital elements $$d(\theta) = \sqrt{(R_E + h)^2 - R_E^2 \cos^2\theta} - R_E \sin\theta$$9 (in Keplerian or Kholshevnikov coordinates) to minimize the functional

$R_E=6371$0

where $R_E=6371$1 is the achieved (u,v)-coverage for source $R_E=6371$2, $R_E=6371$3 is the target, $R_E=6371$4 is a Gaussian convolution kernel, and $R_E=6371$5 denotes beam Fourier synthesis (Bulygin et al., 2024). Minimization proceeds via simplex (Powell/Nelder–Mead) with physically motivated constraints.

Simulation campaigns, including synthetic cases and reconstructions of RadioAstron sessions, demonstrate that the methodology recovers orbital parameters producing beams that closely approximate the desired (u,v)-patterns. Non-uniqueness of the uv-to-orbit mapping is intrinsic, but local minima provide physically valid solutions aligned with mission goals.

6. Trade-offs, Limitations, and Prospects

SCS introduces fundamental trade-offs between spectral efficiency, link robustness, simplicity, and achievable coverage. In DS2D, SUL activation trades reduced L-band spectral efficiency for extended low-elevation and edge-beam availability. The area of switching hysteresis is tuned between rapid adaptation (small $R_E=6371$6) and minimal signaling overhead (larger $R_E=6371$7). Either-or carrier selection avoids multi-band power amplifier nonlinearity, facilitating practical RF design within strict UE constraints (Shrestha et al., 23 Feb 2026).

In interferometry-driven SCS, the inverse-design approach assumes idealized Keplerian dynamics; perturbations, complex operational constraints (sun-avoidance, antenna cooling, radiation), and mission cost factors ($R_E=6371$8, lifetime) may require integration as hard constraints or supplementary cost functions. The mapping inversion does not ensure unique orbital solutions; families exist for a given (u,v)-goal.

Further extensions comprise adaptive SCS (real-time orbit planning), global optimization algorithms (GA/MCMC), multi-satellite coordinated design, and comprehensive physical modeling.

7. Impact and Future Directions

SCS frameworks enable robust, globally accessible communications even in infrastructure-deficient environments, and facilitate high-fidelity astronomical imaging through strategic space asset allocation. The predictive and adaptive selection of spectrum and orbits are central themes, supported by detailed link-budget and geometry-aware modeling. Integrative approaches balancing physical-layer constraints, system-level goals, and operational practicality define current and future research directions (Shrestha et al., 23 Feb 2026, Bulygin et al., 2024). A plausible implication is that the general strategy of SCS—prediction-driven adaptive configuration leveraging space asset geometry—may inform broader classes of hybrid terrestrial–space systems, from next-generation mobile communications to distributed sensing.